## 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 Resea rch Institute 2215 Raggio Parkway Reno, NV 89512

WORKSHOP PARTICIPANT S :

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 E nvironmental 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 Nev ada, 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. E nvironmental 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 PM 10 and PM 2.5 concentrations to fugitive dust sources. Emission models need to be compatible with the spatial scales of atmospheric dispersion models and PM 10 or PM 2.5 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 showsignificant 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 PM 10 or PM 2.5 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 by4 -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 PM 10 or PM 2.5 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 PM 10 or PM 2.5 (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 precursor4 -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, PM 10 , or PM 2.5 ) 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 PM 10 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 particle4 -4Figure 4-1. Example of horizontal flux measurement system around an unpaved road treated with different suppressants (Watson etal., 1996b).4 -5 Figure 4-2. Cumulative horizontal PM 10 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 PM10Flux below Eelvation

Figure 4-3. Cumulative horizontal PM 10 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 PM10Flux below Elevation4 -6 Figure 4-4. Cumulative horizontal PM 10 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 PM10Flux 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 v d , and if the particles are mixed fairly uniformly to a height h d (e.g., the height of the atmospheric mixing layer), then the residence time is essentially h d /v d . For example, if h d = 1 km and if v d = 1 cm/s, then this residence time is 10 5 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 PM 10 concentrations downwind of an unpaved road is illustrated in4 -7 Figure 4-5. While there is substantial scatter in the ratios of downwind to roadside concentrations, it is clear that PM 10 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 PM 10 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 PM10at 2 m agl4 -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 PM 10 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 monitoring4-10

Figure 4-6. Hourly variations in PM 10 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 (PM 10 , PM 2.5 , 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 μ m.

• 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 scanning4 -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 PM 10 non-attainment areas. The second role of receptor models is to independently verify emissions inventories, as in the comparison between inventory and4 -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 PM 10 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 PM 2.5 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 PM 2.5 speciation network at representative community exposure sites throughout the U.S. Spatial distributions of annual average PM 2.5 soil contributions range from 0.5 to 1 μg/m 3 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 PM 10 and PM 2.5 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 PM 2.5 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 PM 2.5 and PM 10 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 often5 -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 PM 10 or PM 2.5 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 PM 2.5 and PM 10 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 PM 10 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 PM 2.5 fraction of fugitive dust emission factors. The extent to which TSP and PM 10 emission factors can be scaled to PM 2.5 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 PM 10 . 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 PM 2.5 and PM 10 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 PM 2.5 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 PM 2.5 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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