Reconciling Urban Fugitive Dust Emissions Inventory 

and Ambient Source Contribution Estimates: 

Summary of Current Knowledge and Needed Research

DRI Document No. 6110.4F

May, 2000

PREPARED BY

John G. Watson and Judith C. Chow

Desert Research Institute

2215 Raggio Parkway

Reno, NV 89512

WORKSHOP PARTICIPANTS:

William Barnard – E.H. Pechan and Associates Inc.

William Benjey – U.S. Environmental Protection Agency

Will Cates – Clark County Dept. of Comprehensive Planning

John Core – Core Environmental Consulting

Chatten Cowherd – Midwest Research Institute

Dennis Fitz – University of California, Riverside

Donald Gatz – Illinois State Water Survey

Dale Gillette – National Oceanic and Atmospheric Administration

David James – University of Nevada, Las Vegas

William Kuykendal – U.S. Environmental Protection Agency

Ronald Myers – U.S. Environmental Protection Agency, MD-14

William Nickling – University of Guelph

Duane Ono – Great Basin Unified Air Pollution Control District

Thompson Pace – U.S. Environmental Protection Agency

Thomas Rosendahl – U.S. Environmental Protection Agency, MD-15

Dale Shimp – California Air Resources Board

W. George N. Slinn – Consultant

Melvin Zeldin – South Coast Air Quality Management District

University and Community

College System of Nevada
	

						


				
			


				
	 	
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4-1

4. MODELS FOR FUGITIVE DUST CONTRIBUTIONS

Estimates of source contributions from fugitive dust and other sources are made using

air quality models. Source-oriented emissions, meteorology, chemical transport models, and

receptor models are often used independently to assess source contributions. A better

integration of these different modeling systems would identify emission mechanisms that are

not adequately represented in any of the models and would improve the attribution of

excessive PM10 and PM2.5 concentrations to fugitive dust sources.

Emission models need to be compatible with the spatial scales of atmospheric

dispersion models and PM10 or PM2.5 measurement networks. These scales (Watson et al.,

1997b) are given below. Distances indicate the diameter of a circle, or the length and width

of a grid square, with a monitor at its center.

• Microscale (10 to 100 m): Microscale pollutant concentrations show significant

differences between locations separated by 10 to 50 m. This often occurs next to

a emission sources, such as a busy roadway, construction site, vent, or short stack.

• Middle Scale (100 to 500 m): Middle-scale pollutant concentrations show

significant differences between locations that are ~0.1 to 0.5 km apart. Middlescale zones of representation are often source-dominated.

• Neighborhood Scale (500 m to 4 km): Neighborhood-scale pollutant

concentrations do not show significant differences with spacing of a few

kilometers. This dimension is often the size of emission and modeling grids used

in large urban areas for source assessment. Sources affecting neighborhood-scale

sites typically consist of small individual emitters, such as clean, paved, curbed

roads, uncongested traffic flow with a small number of heavy-duty vehicles, or

neighborhood use of residential heating devices such as fireplaces and wood

stoves.

• Urban Scale (4 to 100 km): Urban-scale monitors show consistency among

concentrations separated by of tens of kilometers. These concentrations represent

a mixture of pollutants from many sources within an urban complex, including

those from the smaller scales.

• Regional-Scale Background (100 to 1,000 km): Regional-scale concentrations

show consistency among measurements for separations of a few hundred

kilometers. Regional concentrations are often more consistent for secondary

pollutants, such as ozone, sulfate, and nitrate, than they are for primary PM10 or

PM2.5 emissions. Regional-scale concentrations are a combination of naturally

occurring substances as well as pollutants generated in urban and industrial areas

that may be more than 1,000 km distant. Regional-scale sites are best located in

rural areas away from local sources, and at higher elevations.

• Continental-Scale Background (1,000 to 10,000 km): Continental-scale

background concentrations show little variation even when they are separated by
4-2

more than 1,000 km. They are hundreds of kilometers from the nearest

significant emitters. Although these sites measure a mixture of natural and

diluted manmade source contributions, the manmade component is at its

minimum expected concentration.

• Global-Scale Background (>10,000 km): Global-scale background

concentrations represent source emissions transported between different

continents as well as naturally emitted particles and precursors from oceans,

volcanoes, and windblown dust.

4.1 Emission Models

Previous sections have discussed the emissions inventory as a static entity based on

empirical factors related to commonly available data. While emissions inventories provide

useful compilations of information, they will not achieve their full potential until they are

elevated to the status of other air quality models that are applicable to specific situations.

This is especially true for fugitive dust emissions, as these are not confined specific times and

locations as are industrial sources and vehicle exhaust.

Several types of emission models are considered separately: 1) speciated rollback;

2) horizontal and vertical flux; and 3) dynamic activities.

4.1.1 Speciated Rollback

Linear rollback (Barth, 1970; deNevers and Morris, 1975; Cass, 1981; Cass and

McRae, 1981, 1983) is the most commonly used method for control strategy development,

although it is not often identified as such. Rollback assumes that atmospheric concentrations

in excess of background are proportional to aggregate emission rates. Reducing excessive

concentrations of a pollutant to levels below a pre-set standard requires emission reductions

that are proportionally equal to the relative amount by which the standard is exceeded, after

background subtraction. Linear rollback for PM10 or PM2.5 mass emissions identified in

Table 2-1 nearly always targets fugitive dust sources because these are the largest mass

emitters.

A better approach is a speciated rollback that applies to the major chemical

components of PM10 or PM2.5 (i.e., geological material, carbon, sulfate, nitrate, and

ammonia). This requires a speciated inventory that applies chemical profiles to emission

estimates from each source type. Speciated inventories have only been created for specific

modeling studies and are not commonly available in non-attainment areas.

Speciated linear rollback has the fewest complex data requirements, but it also carries

large uncertainties. These uncertainties might not, however, be larger than those associated

with other modeling methods. They may be acceptable for selecting among different

pollution control measures, or at least narrowing the scope of viable alternatives.

Linear rollback does not consider the effects of meteorological transport between

source and receptor or of differences in gas-to-particle conversion for different precursor
4-3

emitters. It is most valid for spatial and temporal averages of ambient concentrations that

represent the entire airshed containing urban-scale sources. The effect of transport from

distant sources located outside the airshed is compensated by subtracting background

concentrations, measured nearby but outside the airshed, from ambient levels prior to

determining needed emission reductions. Linear rollback also assumes for secondary

particles, such as ammonium nitrate and ammonium sulfate, that one of the precursors limits

particle formation.

Whether or not speciated linear rollback is used to develop control strategies, a

speciated emission model is needed to better evaluate emission estimates against ambient

concentrations. Over year-long temporal and spatial averaging times, the ratio of carbon to

geological concentrations above background levels should be similar in air samples and in a

speciated primary particle inventory.

4.1.2 Horizontal and Vertical Fluxes

Fugitive dust emissions are emitted over large surface areas and are not ducted. This

makes their quantification inherently less accurate than emission tests on stacks or other

ducts through which effluents are ejected into the air.

Fugitive dust emission factors are most commonly determined by measuring the

horizontal flux from an emitting area such as a road or vacant lot (Cowherd et al., 1984).

This is accomplished by locating sampling systems with the desired size-selective inlet (TSP,

PM10, or PM2.5) at various elevations downwind of the dust-emitting area. Monitors located

upwind are used to determine the flux into the emitting domain.

Each of the downwind samplers is used to represent the amount of dust carried by the

wind component perpendicular to a plane parallel to the source. Both the wind speed and

concentration vary with height above ground level, so the horizontal flux is calculated

through an area that extends above and below each sampler. These fluxes are added to

obtain the aggregate emission rate from the source, after the flux of particles into the emitting

area has been subtracted. Figure 4-1 shows a typical configuration for making these

measurements. Sampler elevations, distances from the source, and measurement devices

vary among the different studies that have been conducted.

Figures 4-2 through 4-4 show the cumulate horizontal emission fluxes at different

elevations above ground level (agl) for paved roads, unpaved roads, and bare

soil/construction sites. These plots result from many the same downwind profile tests

(Cowherd, 1999) that were used to derive commonly used PM10 emission factors. The most

noticeable feature from these plots is that approximately 60% to 90% of the horizontal

emission flux is detected at elevations less than 2 m agl.

A 10 μm aerodynamic diameter particle has a settling velocity of ~0.3 cm/s, and

would therefore deposit to the surface within ~5 minutes after achieving an elevation of 1 m

above ground level. This corresponds to a travel distance of no more than 1 km in a 3 m/s

wind. Travel distances would be 0.25 km for a 20 μm particle and 4 km for a 5 μm particle
4-4

Figure 4-1. Example of horizontal flux measurement system around an unpaved road treated with different sual., 1996b).
4-5

Figure 4-2. Cumulative horizontal PM10 flux at different downwind elevations above

different unpaved roads (data from Cowherd, 1999).

0%

20%

40%

60%

80%

100%

120%

0 1 2 3 4 5 6 7 8

Elevation above Unpaved Road (m agl)

Percent of PM10 Flux below Eelvation

Figure 4-3. Cumulative horizontal PM10 flux at different downwind elevations above

different paved roads (data from Cowherd, 1999).

0%

20%

40%

60%

80%

100%

120%

0 1 2 3 4 5 6 7 8 9 10

Elevation above Paved Road (m agl)

Fraction of PM10 Flux below Elevation
4-6

Figure 4-4. Cumulative horizontal PM10 flux at different downwind elevations above

different construction and bare soil sites. (data from Cowherd, 1999).

0%

20%

40%

60%

80%

100%

120%

0 1 2 3 4 5

Elevation above Construction and Bare Soil (m agl)

Percent of PM10 Flux below Elevation

under similar wind conditions. Plate-like particles (e.g., clay) fall more slowly than spherical

particles. Upon returning to the surface, supermicron-sized particles can “bounce” and/or

become resuspended (Chamberlain, 1967; Wu et al., 1992; Paw U, 1992). During wind

erosion processes, airborne particles become electrically charged relative to surfaces, and can

be attracted to those surfaces (Schmidt et al., 1998). A fairly well known manifestation of

this charge separation is electrical discharges (“lightning”) near the surface during some dust

storms. The resulting electrical forces can substantially increase the rate of dry deposition of

the airborne particles, a process apparently not currently included in dry deposition models.

If fugitive dust can be lofted from the near-surface atmospheric layer by mechanical

and/or thermally generated turbulence, then particles have potential for residing in the

atmosphere for substantial time periods. If the only removal process is by dry deposition,

with deposition velocity vd, and if the particles are mixed fairly uniformly to a height hd (e.g.,

the height of the atmospheric mixing layer), then the residence time is essentially hd/vd. For

example, if hd = 1 km and if vd = 1 cm/s, then this residence time is 105 sec or approximately

a day.

Low-level particles are likely to deposit to the ground, horizontally impact on nearby

obstructions, or rapidly disperse within a short distance from the point of emissions. The

rapid attenuation of PM10 concentrations downwind of an unpaved road is illustrated in
4-7

Figure 4-5. While there is substantial scatter in the ratios of downwind to roadside

concentrations, it is clear that PM10 is attenuated by ~90% within only 50 m from an unpaved

roadside.

Urban- and regional-scale dispersion models usually consider fugitive dust emissions

at elevations much higher than 2 m agl. Effective emission heights of 5 to 10 m agl are often

used so that a plume can develop downwind of a site. Most models are unable to resolve

terrain variations of 1 to 2 m and do not account for the effects of horizontal impaction on

vegetation and obstructions such as trees, shrubbery, and buildings.

There is little published information on the quantitative effect of nearby obstructions

on low-level dust emissions, but it is common practice to place obstructions such as

greenbelts (Kapoor and Gupta, 1984; Gupta and Kapoor, 1992) near visible dust emitters.

For example, Slinn (1982) shows that dust deposition velocities through a eucalyptus forest

can be five to ten times higher than gravitational settling velocities with wind speeds of 5 to

10 m/s.

Vertical flux is an alternative to horizontal flux as a method to estimate fugitive dust

emissions (Gillette, 1977). Only a fraction of the horizontal flux moves upward, with this

fraction dependent on a variety of meteorological, aerosol, and surface conditions. Vertical

Figure 4-5. Attenuation of PM10 concentrations with distance from an unpaved road

(Watson et al., 1996b).

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50

Distance from Unpaved Road (m)

Normalized PM10 at 2 m agl
4-8

flux emissions can become available for transport to distances beyond the immediate vicinity

of the source. Vertical flux is proportional to particle density, surface friction velocity, and

the difference between particle concentrations at different elevations above ground level.

These variables can be estimated from the same measurements used for horizontal flux

measurements with a configuration similar to that of Figure 4-1 (Nickling and Houser, 1999).

It appears that currently used emission factors based on integrated horizontal flux

adequately represent suspendable dust, the amount that leaves a dust-generating surface, but

they do not adequately represent transportable dust, the fraction of suspendable dust that is

likely to travel more than a few kilometers from the emitter.

More realistic estimates for the vertical components of the dust fluxes from fugitive

emissions are needed to evaluate fugitive-dust concentrations beyond the immediate vicinity

of sources (e.g., Johnson, 1983; Shao and Leslie, 1998). Shao and Leslie (1998) set the

vertical flux for each particle-size range proportional to the horizontal flux, with the

proportionality “constant” dependent on particle size. There is no dependence of the vertical

flux on atmospheric stability.

Modeling of vertical fluxes of particles from fugitive emissions should proceed on

two fronts. For regional- to continental-scale models, algorithms should be developed and

tested to estimate a representative vertical flux (e.g., at the lowest layer of the air quality

model, which is typically 30 m) for emissions in each grid square. This algorithm could

incorporate a relatively simple boundary-layer formulation driven by the larger model’s

regional-scale meteorological fields. For practical applications, simple plume models that

incorporate deposition and atmospheric stability, especially for high-wind-speed cases, need

to be interfaced with vertical emission flux estimates.

4.1.3 Dynamic Activity Models

Dynamic activity models refer to computerized systems that have access to databases

that may change over periods of months, weeks, days, or even hours. Such models could be

assembled after the fact by compiling information specific to modeled episodes. They could

also be created to access certain databases in real time so that the information could be used

to modify activities to minimize fugitive dust emissions from certain activities such as

construction. The structure of dynamic activity models can range from simple spreadsheets

to complex physical interaction models that include meteorological processes to determine

windflows, turbulence, humidity, and other factors that can influence fugitive dust emissions.

Spreadsheets are the easiest and most common means for constructing dynamic

activity models (Kuhns et al., 1998). This approach is most appropriate to a relatively small

area where the number of separate activity databases is limited and where locations can be

precisely defined. Each cell in the spreadsheet can represent a grid, different worksheets can

represent different activity databases, and relationships between activity and emission rates

can be established by calculations in linked worksheets. The spreadsheet approach is most

appropriate when used in rollback calculations, where area-wide, rather than spatially

resolved, emissions are needed.
4-9

Where complex spatial emission inputs are needed, a Geographical Information

System (GIS) provides the most flexible framework. Many of the available activity

databases (e.g., roadway networks, land use, soil characteristics, traffic volumes) have

irregular shapes onto which an arbitrary grid system must be imposed. This grid system may

change for different applications. For the most part, existing spatial and temporal data sets

compiled for purposes unrelated to air quality are obtained to estimate dust-generating

activity.

While spatial activity data sets may be obtained with seasonal, and possibly even

monthly variations, it is unlikely that more highly resolved information will be available. In

this case, temporal profiles can be imposed on an average emission rate for each source type.

These profiles can be determined theoretically or empirically and should have diurnal as well

as day-of-the-week variability. Figure 4-6 shows an example of diurnal variability in PM10

concentrations at different sampling sites near major roadways in Las Vegas, NV, on

wintertime weekdays and weekends. Although non-traffic sources influence these

concentrations, the weekday morning and evening rush hour contributions from vehicle

exhaust and paved road dust are clearly discernable in the top panel. The bottom panel

shows a distinctly different diurnal profile on weekends. Traffic profiles can also be

developed by vehicle counting and by on-road remote sensing methods.

4.2 Dispersion Models

Source-oriented dispersion models use the outputs from emission, meteorological,

and chemical models to estimate concentrations measured at receptors. Source models

include mathematical simulations of transport, dispersion, vertical mixing, deposition, and

chemical mechanisms to represent transformation. The most common source dispersion

models are Gaussian plume, puff, and grid formulations. Gaussian plume models (Schulze,

1990; Freeman et al., 1986; Schwede and Paumier, 1997) are most often associated with the

straight line wind model and estimate a bell-shaped concentration field in the vertical and

horizontal directions from the wind direction. These models are commonly used to evaluate

potential effects of primary emissions from ducted sources, such as industrial stacks. Puff or

trajectory models treat emissions from a variety of sources as independent entities that are

moved in a curvilinear wind field generated by a diagnostic or prognostic wind model. Grid

models transfer pollutants between boxes with pre-defined vertical and horizontal dimensions

(Bowman et al., 1995; Byun and Ching, 1999; Byun and Dennis, 1995; Yamartino et al.,

1992). Current dispersion modeling of particulate matter emissions is generally

accomplished using gridded Eulerian chemical-transport models (CTM's) on urban to

regional scales. Smaller scales are often modeled by less complex Gaussian plume or

trajectory models.

Meteorological models consist of straight line, interpolation (termed diagnostic), and

first principle (termed prognostic) formulations, with increasing levels of complexity and

requirements for computational and data resources. The straight line model is applied to

hourly wind directions from a single monitor, assuming an air mass travels a distance equal

to the wind velocity in the measured direction, regardless of the distance from the monitoring
4-10

Figure 4-6. Hourly variations in PM10 concentrations acquired by beta attenuation monitors

(BAM) at different urban residential (Walter Johnson), commercial (East Charleston, City

Center) and industrial (Craig/Bemis) sites in Las Vegas, NV on: a) weekdays, and b)

weekends (Chow and Watson, 1997).
4-11

site. This model is applicable for a few hours of transport in flat terrain, typically for

evaluating a single emission source. Interpolation models integrate wind speed and direction

data from multiple measurement locations, including upper air measurements provide by

remote sensors or balloon launches. The more advanced of these models allow barriers, such

as mountains, to be placed between monitors. Wind fields, therefore, show different

directions and velocities at different horizontal and vertical positions. Interpolation wind

models are applicable to domains with a large number of well-placed monitors and for

estimating the movement of air masses from many sources over transport times of more than

half a day. The number and placement of monitors, especially upper air monitors, is

especially important in mountainous terrain and in coastal areas where winds are unusual.

Advanced chemical transport models are driven with meteorological data taken from

prognostic models (Stauffer and Seaman, 1994; Seaman et al., 1995; Koracin and Enger,

1994) that embody scientists’ best knowledge of atmospheric physics and thermodynamics.

These models employ basic equations for conservation and transfer of energy and

momentum. These meteorological models are computationally intensive, often requiring

supercomputers but are becoming more practical and cost-effective as workstation and

desktop computers become more powerful. Modern versions use “four-dimensional data

assimilation” that compare model-calculated wind, humidity, and temperature fields with

measurements and “nudge” model outputs toward observations. A more complex

meteorological model is not necessarily a better model for a specific application. The MM5

meteorological model has been adopted as the platform for several air quality studies

(Seaman et al., 1995).

Emission, chemical transport, and meteorological models must be applied to the same

temporal and spatial scales. Horizontal dimensions of chemical transport models range from

as low as 1 to 2 km for urban-scale applications to 10 to 50 km for regional-scale

applications. Vertical dimensions are treated as layers at different elevations above ground

level with thickness ranging from 20 m to 2,000 m. Most of the model inputs and outputs are

assumed to be homogeneously distributed within rectangular solids with these dimensions.

This assumption implies that inputs and outputs with large spatial variability within these

dimensions will not be well simulated by these models.

Emission data needed for regional modeling includes both emission fluxes and some

characteristics of the emissions and their sources. As noted in Section 4.1, fugitive dust

emissions are derived from horizontal flux estimates. Vertical emission fluxes are better

simulated by chemical transport models. Emissions inventory inputs to chemical transport

models include: 1) source location identification (coordinates for point sources, political

identifiers for political areas); 2) pollutant identification (PM10, PM2.5, or other size

fractions); 3) emission flux (mass per unit time); 4) source type; 5) pollutant emission

controls (if any); 6) effectiveness of controls (if any); 7) applicable time frame of inventory;

and 8) for point sources with stack height, identifier, diameter, flow rate, exhaust

temperature, and exit velocity. There is large contrast between the detailed information

available for industrial sources and the less complete information for area and mobile

sources.
4-12

4.3 Receptor Models

The Chemical Mass Balance (CMB) air quality model is one of several models that

have been applied to air resources management. As described in Section 2, receptor models

use the chemical and physical characteristics of gases and particles measured at source and

receptor to both identify the presence of and to quantify source contributions to receptor

concentrations. Receptor models are generally contrasted with dispersion models that use

pollutant emission rate estimates, meteorological transport, and chemical transformation

mechanisms to estimate the contribution of each source to receptor concentrations. The two

types of models are complementary, with each type having strengths that compensate for the

weaknesses of the other.

The CMB consists of a solution to linear equations that express each receptor

chemical concentration as a linear sum of products of source profile abundances and source

contributions. The source profile abundances (i.e., the mass fraction of a chemical or other

property in the emissions from each source type) and the receptor concentrations, with

appropriate uncertainty estimates, serve as input data to the CMB model. In order to

distinguish among source type contributions, the measured chemical and physical

characteristics must be such that they are present in different proportions in different source

emissions and changes in these proportions between source and receptor are negligible or can

be approximated. The CMB calculates values for the contributions from each source and the

uncertainties of those values.

Receptor models do not need emission rates from an emission model, but they do

need chemical profiles that allow one source to be distinguished from other sources. For

quantitative source apportionment, source profile properties must meet the following criteria:

1) their abundances (mass fraction) are different in different source types; 2) their

abundances do not change appreciably during transport between source and receptor (or such

changes can be simulated by measurement or modeling); and 3) their abundances are

reasonably constant among different emitters and operating conditions for a selected source

type. These profiles are used in emission inventories to separate mass emissions into

chemical component emissions and in receptor models to apportion ambient concentrations

of suspended particles to sources.

Additional chemical and physical properties beyond the commonly measured

elements, ions, and carbon need to be examined to determine which ones can meet these

criteria in practical source apportionment applications. Several types of analyses that might

meet these needs are:

• Appearance: Optical microscopy provides an overview, albeit semi-quantitative,

of the shape, size, mineralogy, and type of different particles with geometric

diameters larger than ~2 μm.

• Soluble and insoluble inorganic elements and compounds: X-ray

fluorescence, proton-induced x-ray emission, instrumental neutron activation

analyses, atomic absorption spectrophotometry, inductively coupled plasma

atomic emission spectroscopy, x-ray diffraction, computer automated scanning
4-13

electron microscopy, ion chromatography, and automated colorimetry have been

applied, sometimes in conjunction with different extraction and selective sample

preparation techniques, to quantify a variety of elements and crystalline

structures.

• Carbon groupings: Organic and elemental carbon, thermal desorption and

pyrolysis patterns, and solubility in different organic solvents using thermal

manganese oxidation, thermal optical reflectance, thermal optical transmittance,

Fourier transform infrared spectroscopy, thermal desorption gas chromatography

and mass spectrometry, and sequential solvent extraction are possibilities.

• Specific organic compounds: Pesticides, herbicides, cellulose, and many other

specific organic compounds may be analyzed by solvent extraction followed by

gas or liquid chromatography with different detectors.

• DNA, toxins, microbes, and bacteria: Biologically specific tests can be applied

to characterize these substances.

• Isotopes: Low-level radioactive counting, dilution mass spectrometry, and

accelerator mass spectrometry are potential methods to quantify unique isotopic

abundances in geological materials.

A systematic survey of the extent to which these observables are found in different

sources is needed to further specify source attribution using receptor models.

4.4 Interaction Among Models

Given the dynamic nature of PM emissions, more accurate representations may be

gained by modeling the emissions as described in Section 4.1. Emission modeling needs to

bring emissions to the same temporal and spatial scales, typically 1 to 10 km and 1 hour

resolutions, that are used for the meteorological and chemical/transport models.

In current practice, emissions are considered static and do not interact with the

modeling process. A better approach would apply a dynamic emission model that uses

meteorology and changing activity databases as inputs. This dynamic emission model would

account for the transportable fraction of dust emissions that is consistent with the spatial

scales of the other models. For microscale modeling of neighborhood impacts, this might

include horizontal fluxes in the lowest levels. For urban and regional modeling with 1 to 10

km spatial domains, only horizontal fluxes at higher elevations, or vertical fluxes, would be

used to represent transportable particles.

Receptor models such as the CMB should be included in this interaction. The first

role of receptor models is the direct apportionment of ambient concentrations to their

sources. This apportionment in and of itself has been of great utility in developing control

strategies in PM10 non-attainment areas. The second role of receptor models is to

independently verify emissions inventories, as in the comparison between inventory and
4-14

source apportionment studies presented in Section 2. Impediments to CMB/inventory

reconciliation are the lack of specificity of chemical components that distinguish different

fugitive dust source types from each other. Receptor models also offer a framework for

evaluating the outputs of complicated air quality models. Source-oriented models produce a

pattern of concentrations and uncertainties similar to CMB source profiles that could be used

by the CMB for a multivariate fit to the ambient data. This is a more complex, and

potentially more accurate “profile aging” process, as represented in a simpler context by

Friedlander (1981).

4.5 Hypothesis Testing

The preceding discussion allows the following conclusions to be drawn about

hypotheses five and six:

• Insufficient and uncertain activity levels. Current emissions inventories are not

dynamic. They do not easily adapt to the changing times and locations of

activities that affect ambient concentrations. Dynamic emission models are

needed that seamlessly interact with meteorological, chemical/transport, and

receptor models. Chemical source profiles need to be enhanced with more

specific markers for different fugitive dust source types to verify changes in

activity through receptor measurements. Dynamic databases of the locations and

timing of emissions need to be incorporated into emission estimation methods.

• Insufficient accounting for injection heights, deposition losses, and horizontal

impaction losses in dispersion models. This is the most evident and

documented cause of discrepancies between emission estimates and ambient

source contributions from fugitive dust. Commonly used fugitive dust emission

factors estimate integrated horizontal fluxes from ~1 m to ~10 m above ground

level. Approximately 60% to 90% of these fluxes are below 2 m and will not

travel long distances (> a few km) from the emitter. Most receptor model source

contributions are estimated at community representative, urban-scale, monitoring

sites that are specifically selected to be uninfluenced by nearby sources. Most

source-oriented models estimate source contributions averaged over dimensions

of 1 km to 10 km. Transportable dust emissions need to be distinguished from

suspendable dust that is currently used to estimate emissions. Transportable dust

emissions depend on the spatial scale of modeling. For middle-scale models,

current estimates are probably adequate. For neighborhood-, urban-, and

regional-scale modeling, the transportable fraction is likely to vary. Methods to

estimate this variability need to incorporate vertical fluxes and removal processes

due to vertical deposition and horizontal impaction. A first approximation for

transportable dust that moves beyond a few kilometers would eliminate the

horizontal flux within the first 2 m above ground level. This would reduce

current fugitive dust emission estimates by ~75%.
5-1

5. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

Previous sections of this report identified differences between emission estimates and

ambient geological contributions, estimated the magnitudes of these differences that could be

caused for different reasons, and evaluated the technical bases for currently applied emission

estimation procedures. This section summarizes the results of this examination and specifies

research projects that can be conducted immediately, in the short term (1 to 2 years), and

over a longer term (2 to 5 years) to improve scientific understanding of and quantitative

estimation of fugitive dust emissions.

One of the methods used to identify potential discrepancies was to compare emissions

estimated for a single state, California, with national inventory estimates for that state. The

methodology is essentially the same for these inventories, but the emission factors, activity

databases, and control measures applied are substantially different. These differences

resulted in California’s urban dust emissions from unpaved roads, paved roads, and

construction being only 60% of those estimated by the national inventory. Much larger

discrepancies were found for specific areas and source types.

5.1 Summary

Subtracting secondary sulfate and nitrate contributions from PM10 prior to estimating

the fugitive dust fraction in source apportionment studies results in a higher fractional

geological contribution. There is still a large discrepancy between the dust fraction in

emissions inventories and the fractional dust contribution to ambient mass concentrations.

Although PM2.5 source apportionment studies are limited, it is likely that discrepancies will

be larger for this size fraction in which geological contributions are much lower. A more

systematic examination of the fugitive dust proportion in ambient samples will be possible

when data become available from EPA’s PM2.5 speciation network at representative

community exposure sites throughout the U.S.

Spatial distributions of annual average PM2.5 soil contributions range from 0.5 to 1

μg/m3 at background sites in the United States as determined by the Interagency Monitoring

of PRotected Visual Environments (IMPROVE) network. These should be subtracted from

urban PM10 and PM2.5 fugitive dust contributions prior to determining proportions for

comparison with local emissions inventories. Higher soil contributions on a few days in

Florida and other areas may be caused by long-range transport of Sahara dust that are not

accounted for in inventories. More extensive IMPROVE monitoring in the eastern U.S. will

supplement urban speciated PM2.5 monitoring sites to improve estimates of background

contributions to urban fugitive dust levels. More specific chemical and physical markers

need to be developed and measured in source and receptor samples to distinguish background

from other fugitive dust sources, as well as different types of dust emitters from each other.

Temporal and spatial averaging periods for fugitive dust emissions are not the same

as those for ambient PM2.5 and PM10 measurements. National inventories are specific to

statewide averages and are based on activities averaged over one year or more. State and

local inventories rarely have spatial resolutions better than the size of a county and temporal

resolution of less than a month. These averaging times contrast with intermittent and often
5-2

random events that raise dust into the atmosphere to affect ambient samples that are averaged

over 24-hours or less. Discrepancies between inventory and ambient concentrations are

especially evident in large counties that contain population centers with PM10 or PM2.5

monitors. A comparison between California and national emissions for unpaved roads,

paved roads, and construction at the county level showed substantial differences by county

and by source category, even though the statewide totals were 40% lower than the national

totals. These are probably due to differences in the statewide activities that are allocated to

county in the national inventory compared to the countywide activities that are aggregated to

state in California. Most of the emissions from unpaved roads and windblown dust occur in

non-urban areas far from the monitor, but they are included in the inventory used to assess

source contributions at that monitor. Even within an urban area, neighborhood-scale studies

show that there may be large differences between dust contributions for monitors separated

by a few kilometers. To improve the relationship between emissions and source

contributions, the spatial and temporal resolutions of emissions inventories need to be made

more compatible with the zone of representation and averaging times of PM monitors.

Emission factors for unpaved road and paved road dust emissions are reasonably

consistent from one test to another. Major reductions were found for California when

location-specific values were used for silt content and loadings in place of the default values

used for the national inventory. Construction emission factors used in the national inventory

overestimate construction emissions because they assume that the heavy earthmoving

activities on which they are based occur throughout the duration of the project. California

inventories use construction emission factors derived from the actual processes that engender

those emissions and are an order of magnitude lower than those used for the national

inventory.

National inventory activity levels do not accurately reflect the activities at county or

finer resolutions. National estimates are made statewide, then allocated to counties based on

activity surrogates. There are clear discrepancies between this approach and the California

inventory that builds the statewide inventory from countywide activity estimates. The county

specific data better allocate emissions than the statewide national activity databases. On the

other hand, the national inventory assumes control effectiveness values that are not borne out

by fugitive dust control demonstration studies. There are still large uncertainties and

variabilities in the activity databases for both inventories.

Current emissions inventories are not dynamic. They do not easily adapt to the

changing times and locations of activities that affect ambient concentrations. Dynamic

emission models are needed that seamlessly interact with meteorological, chemical/transport,

and receptor models. Chemical source profiles need to be enhanced with more specific

markers for different fugitive dust source types to verify changes in activity through receptor

measurements. Dynamic databases of the locations and timing of emissions need to be

incorporated into emission estimation methods. The wind erosion portion of the national

inventory represents a relatively small number of individual events that are averaged over a

year. These events should not be included in annual average estimates that are intended to

represent continuous and repetitive processes.
5-3

There is insufficient accounting for injection heights, deposition losses, and

horizontal impaction losses in dispersion models. The scale and temporal resolution of

emissions inventories is incompatible with the needs of dispersion models. This is the most

evident and documented cause of discrepancies between emission estimates and ambient

source contributions from fugitive dust. Commonly used fugitive dust emission factors

estimate integrated horizontal fluxes from ~1 m to ~10 m above ground level.

Approximately 60% to 90% of these fluxes are below 2 m and will not travel long distances

(> a few km) from the emitter. Most receptor model source contributions are estimated at

community representative monitoring sites that are located for minimal influence from

nearby sources. Most source-oriented models estimate source contributions averaged over

dimensions of 1 km to 10 km. Transportable dust emissions need to be distinguished from

suspendable dust that is currently used to estimate emissions. Transportable dust emissions

depend on the spatial scale of modeling. For middle-scale models, current estimates are

probably adequate. For neighborhood-, urban-, and regional-scale modeling, the

transportable fraction is likely to vary. Methods to estimate this variability need to

incorporate vertical fluxes and removal processes due to vertical deposition and horizontal

impaction. A first approximation for regionally transportable dust (i.e., dust that is likely to

travel more than a few kilometers) could assume that horizontal flux within the first 2 m

above ground level is removed by nearby surfaces. This would reduce current fugitive dust

emission estimates by about 75%.

5.2 Conclusions

• National inventories do not accurately estimate emissions from fugitive dust

sources that affect ambient PM2.5 and PM10 concentrations for a variety of

reasons. Both negative and positive biases in emissions result, but the net effect is

to overestimate emissions from fugitive dust sources relative to those from other

sources in the inventory.

• Suspendable particles are not transportable particles. Available data shows that

~75% (ranging from ~60% to ~90%) of suspended PM10 remains within 1 to 2 m

above ground level. These particles deposit to the surface or impact on nearby

vertical structures within a few minutes after suspension. Although horizontal

fluxes commonly used in empirically-derived emission factors represent the mass

of dust particles suspended from a surface, they do not represent the mass

entrained into the atmosphere and transported over distances of more than a few

kilometers.

• Source and receptor models do not represent the same spatial and temporal scales

as emissions inventories. Better integration with and interaction among emission,

meteorological, chemical/transport, and receptor models is needed. This

interaction should help to identify and reconcile deficiencies in all of the

modeling components.
5-4

• State and local values for emission factors and activities yield lower emission

estimates, at least for California. Several of these emission factors and activities

should be used in national inventories.

• Emissions inventories treat fugitive dust emissions as continuous processes,

whereas they are intermittent processes that depend on many meteorological and

activity variables. This causes a positive bias in dust emission estimates.

• Few empirical tests are available for the PM2.5 fraction of fugitive dust emission

factors. The extent to which TSP and PM10 emission factors can be scaled to

PM2.5 is uncertain.

• Paved and unpaved road dust emission factors have been found to be of similar

form and magnitude for many independent tests for TSP and PM10. Silt loadings,

silt fractions, and activity levels used in national inventories appear to

overestimate emissions from these sources, at least with respect to California.

• Existing tests for emission factors are biased toward the highest emitters, yet they

are applied to a population of activities that encompasses a wide range of

emission magnitudes. Lower-emitting representatives of a source category are

poorly characterized. This is especially true for construction emission estimates

that characterize the earth moving portion of a project and not the construction

activities.

5.3 Immediate Recommendations for National Emissions Inventory Improvement

• Review construction emission factors and compare them to those used in

California. Consider adopting similar factors for the national inventory.

• Use measurements from previous fugitive dust emission tests to estimate

horizontal dust fluxes above elevations of 2 m. Create separate estimates for

emissions above these heights that can be used for different modeling and

planning purposes. Urban- and regional-scale source or receptor modeling could

use the >2 m above ground level horizontal fluxes. Preliminary estimates given

in this report indicate that >2 m horizontal fluxes are ~25% of total horizontal

fluxes. This approximation needs more scientific justification.

• Conduct countywide comparisons between national inventory and independently

compiled statewide inventories. Use the results of these comparisons to identify

the availability of or need for local information that affect suspendable dust

surface loadings and dust-generating activities.

5.4 Short-Term Recommendations for National Emissions Inventory Improvement

• Create and apply methods to estimate vertical flux, as well as horizontal flux,

from fugitive dust sources and add vertical flux estimates to modeling inventories.
5-5

Revise the current emission measurement methodology such that vertical fluxes

and horizontal fluxes above selected elevations are incorporated into emission

factors and are reported in inventories.

• Conduct additional emission tests of unpaved roads, paved roads, construction,

and other earth-moving emissions that apply vertical and horizontal flux methods

and are specific to PM2.5 and PM10 particle sizes. These tests should represent a

variety of areas in the eastern and western United States.

• Conduct detailed studies of temporal variability for underlying activities

(including control measure effectiveness) that create dust. Determine

representative temporal profiles for diurnal, weekly, monthly, and annual

emissions for each source type. Determine the extent to which reservoirs of dust

are depleted and incorporate these into emission models.

• Create a modeling framework that integrates emission, meteorological,

chemical/transport, and receptor models. This framework would use the

meteorological model to estimate wind speed, wind direction, and moisture that

affect fugitive dust emission rates. Receptor models applied to ambient data

would be used to estimate source contributions at receptors and to reconcile

emissions with these contributions on an area-specific basis.

5.5 Long-Term Recommendations

• Modify existing air quality modeling software to better represent the vertical flux,

deposition, and transport of dust for different spatial scales. Develop, test, and

apply more realistic middle- and neighborhood-scale mathematical models to

represent dust concentrations at receptors near a variety of dust emitters.

• Develop a complete, GIS-based, emission model that can be applied to

neighborhood, urban, and regional scales. This model would include and update

commonly available activity databases such as land use, roadways, and soil

surveys. It would provide for the use of temporal profiles that allow for daily,

weekly, monthly, and annual emission estimation. The model would also contain

fugitive dust source profiles suitable for receptor model source apportionment and

for creating speciated inventories that can be used for rollback modeling. It

should propagate input data uncertainties, project emissions under different

development scenarios, and allow for alternative emission factors and activity

databases to estimate the same emission rates.

• Compile dust characteristics, including surface loadings, chemical compositions

(source profiles), and suspendable particle content (silt or other) for representative

locations throughout the U.S. Include these in a documented database and apply

them to development of emission estimates. Priorities should be given to airsheds

that operate PM2.5 chemical speciation monitors. A protocol that combines

systematic grab sampling and laboratory analysis with continuous on-road

measurements is needed for this to be practical.
5-6

• Calculate source contributions with receptor models applied to data from the

national PM2.5 speciation network. Reconcile these contributions with fugitive

dust emission estimates from these areas and with background concentrations

from nearby IMPROVE monitoring sites. Use the results of this reconciliation to

further identify and ameliorate emission modeling deficiencies.

• Identify and characterize chemical or physical components in different fugitive

dust sources that allow fugitive dust sub-types to be distinguished from each other

in receptor samples. Apply these characterization methods to receptor samples,

quantify contributions at representative distances from sub-type emitters, and use

the results to improve fugitive dust emission estimation methods.

• Define and conduct experiments that increase understanding of vertical flux,

deposition, and removal by surrounding barriers. Such experiments might include

eddy-correlation micrometeorological measurements of positive and negative

vertical fluxes, balances of airflow through and over tree stands downwind of dust

emitters, and measures of particle rebound and filtration over short vegetation,

such as grass.

• Develop and apply new methods to estimate fugitive dust emissions using realtime and remote sensing methods. These methods would be used to verify

emissions from the more established and commonly applied methods as well as to

better understand the physical interactions between emissions and the atmosphere.
 

 	

	

	

	
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