### Methodology to Estimate the Transport able Fraction (TF) of Fugitive Dust Emissions for Regional and Urban Scale Air Quality Analyses

Thompson G. Pace, US EPA (8/3/2005 Revision)

Background and Introduction For a number of years air quality analysts ha ve recognized that the ambient impact of fugitive dust sources is substantially lower than emissions inventories would suggest. Analysis of the chemical species collected by ambient air samplers suggests that the modeling process may overestimate PM2.5 from fugitive dust sources by as much as an order of magnitude. This overestimation creates problems for t hose involved in both PM2.5 and regional haze planning and the de termination of conformity budgets and significance determinations. Most experts agree that this overestimation is due to a combination of shortcomings in the inventory-modeling process: 1) faulty emission factor algorithms, 2) imprecise or difficult to obtai n activity data to apply these algorithms (including inability to account for the eff ect of actual meteorological conditions on emissions), 3) the multiplier used to infer PM2.5 from PM10 emissions, and 4) modeling deficiencies (especially in the treatment of particles near th eir point of emissions). The ambient air sample collection and analysis is believed to be a bette r estimate of overall fugitive dust impact on the environment becau se of these issues with the inventorymodeling process.

Fugitive dust categories of interest incl ude unpaved and paved road dust, dust from highway, commercial and residential constructio n and agricultural tilling. Of these, unpaved roads are the highest single emissi ons category, accounting for about one third of non-windblown fugitive dust emissions. Th is is followed in importance by dust from tilling, quarrying and other earthmoving. No te: windblown dust from agricultural and other exposed land is also important, but the transport fraction values suggested in this paper are not recommended for appl ication to windblown dust sources.

In the mid 1990's, the U.S. Environmental Protection Agency's (EPA) Office of Air Quality Planning and Standards (OAQPS) began to use a factor to "adjust" the fugitive dust emission estimates in regional modeling analyses to obtain be tter agreement between the regional model results and ambient data. This adjustment was an ad hoc "divide-the inventory-by-four" approach to reduce the discrepancy between modeling and ambient data. The adjustment factor was concei ved as an interim approach until a thorough investigation could identify which specific pr oblems in the inventory and model were causing the discrepancy. Since the late ' 90s, the EPA has been actively working to understand the nature of those specific pr oblems. This paper discusses some recent studies and proposes a conceptual model to ap proximate the dust removal near the source
that is not accounted for in either the curre nt emissions inventories or commonly used regional scale air quality models.

DRI / EPA Workshop The EPA/OAQPS and the Desert Research Institute (DRI) conduc ted a workshop in 2000 to begin the process of u nderstanding why modeled and monitored crustal materialfractions do not agree. OAQPS docum ented that the field measurem ents underlying the
dust em ission estim ates were generally taken within 5-10 m e ters of the source and that on average, about two-thirds of the dust plum e is less than 2 m e ters above ground level at
the location where the m easurem ents are m a de. Based on this inform ation and other workshop discussions, the workshop concluded th at since the dust plum e is still turbulent and very close to the ground, substantial dust removal processes can occur near the source (probably within seve ral hundred m e ters), includ ing impaction on land cover (vegetation and structures) and other proces ses that m a y enhance deposition on a local
scale. It also concluded th at regional air quality models (a s they are cu rrently app lied) do not adeq uately accou n t for injection hei ght, d e position los s es and im paction losses near fugitive dust em ission sources. They not ed that in practice, the fraction rem oved by
surface cover is variab le and that add ition a l testin g is n eeded (Watson and Chow 2000).

WRAP Ex p ert Panel on Fugitive Dust
The DRI / EPA W o rksh op was followed by the fo rm ation of an Expert Panel on Fugitive Dust, sponsored by the Western Regional Ai r Partnership (WRAP) and chaired by Dr. Richard Countess. The panel concluded that not all suspended part icle s a r e tr anspor te d long distances. Specifically, the report supported the conclusion of the DRI W o rks hop
which was that m u ch of the ground level fugi tive dust em issions from soil disturbed by

m a n’s activities are likely to be rem oved close to the source. The low release height and
turbulence leaves th e particles tem p orarily cl ose to the ground where they are subject to

rem oval by impaction on nearby horizontal a nd vertical surfaces, including vegetation and structures. The Countess report reco mm ended field studies to expand upon the current knowledge of the rem oval effectiven ess of trees, desert shrub and buildings (Countess 2001) and several studies were conduc ted in response to this recommendation.

The Role of Surface Cover (Vegetation & St ru ctures) in Removal of A i rborne Dust Early research into the general area of dust rem o val was done by Slinn for the U. S. Dept. of Energy. Much of Slinn’s work focused on particle rem oval from air flowing above a tree canopy, but he also discu ssed the concept of a “stilli ng zone” within and below the canopy. W i thin the stilling zone, wind velocity is so m u ch reduced that particles have

am ple tim e t o settle to the ground or im p act on the canopy or groundcover (Slinn 1982).

W i ndbreaks have long been a staple of soil erosion prevention, although m o st of the research has focused on the use of windbreaks p l aced upwind of a field to reduce the wind speed (and thus ero s ion) over th e field. More recen t work has focused on the
effectivenes s of vegetation as a rem oval m echan ism

. Anecdotally, researchers feel that the forest is a very good filter, bo th h o rizon t ally and vertically . Moreover, field tes t s suggest that the transm ittan ce of dust through a windbreak is close to the optical

transm ittance. In other words, if the folia ge is d e nse enough to block lig ht, it also effectively filters particles (Ci onco 2002, Raupach 1999, Raupach 2001).

Thus, the combined work of Slinn, Cionco and R a upach, the DRI workshop and the WRAP Expert Panel on Fugitive Du st sugges t s that fugitiv e dust pa rticles have am ple opportunity to be rem oved near the source, through im paction or filtration onto vegetation or structures or by other deposition m echanis m s . The effect of land cover is

2expected to be highly variab le, depending on the nature and proxim ity of vegetation to
dust sources

. They note that su rface cover th at is taller, den s er and clos er to the sou r ce captures a larger am ount of the particles, with the most capture occurring when a narrow source is surrounded on both sides by tall, dense vegetation such as a road within a forest. However, Cowherd and Pace (2002) note that pa rticles transp orted toward (not generated

am ong) non-porous su rfaces such as buildings or very dense vegetation m a y be diverted
above or aro und those su rfaces.

Mechanism s other than impaction an d filtra tion b y surface co ver m a y also reduce particles very near the source, while the plum e is com p act and close to the ground. These

m echanis m s include electrostatic forces and thermophoresis (which could enhance deposition o n to the earth ’ s surface an d low ground cover very near th e sou r ce) and particle agglom eration w ithin the co mpact plume (which could enhance gravitational settling). These m echanism s aren’t as likely to capture particles in therm a lly buoyant or elevated plu m es because those plum es rise above the land cov e r m o re quickly. Field testing is needed to quantify these m ech anism s . (Cowherd and Gebhart 2003, Flagan 2001, Gieseke 1972).

Fugitive Dust Emission s, Surface Cover Pa rticle Remova l and Air Quality Mod e ls As noted above, ground level fugi tive dust em issions are m easured adjacent to their point
of e m ission; thus, as with em issions from all types of sources, they m a y be m odified or

even rem oved from the atm o sphere before they reach recepto rs. Thus, em ission
estim ates are only m eaningful on a very local s cale around their release point. In general, this does not present problem s if one is concer ned with effects on this local scale or w h en models are a v ailab l e to tr eat th e poten tia l m odification or rem o val. However, these

em issions are often used to support analyses on an urban or even larger scale. This could involve inventory tracking budge ts (e.g., conform ity) but they are also used in grid models whereby they are imm e diately introduced into m odel grids m u ch larger than the
scale of the rem oval processes discussed above.

Recently, s e veral researchers and m o deling p r acti tioners have identified is sues associated with how air quality m o dels treat gro und le vel fu gitiv e dust em issions and how current models and modeling practices can lead to an un derestim ate of particle removal. Some of these issues were recently docum ente d by staff at the Idaho Departm e nt of
Environm ental Quality (Idaho DEQ 2003). They concluded that Eulerian grid m odels generally over-predict co arse particle (2.5 ~ 10 μ m) concentrations, due prim arily to the
fact that thes e m odels artificially re-m ix the particles in the lo west m odeling layer at each
tim e step (Dong, 2003). DRI, in their work for DOD, also evaluated the rem oval

m echanis m s in the Atm o spheric Diffusion Equation and in IS C3. They found the ISC better suited to analyze near fi eld dispersion (Etyem ezian 2003).

Irwin (2003) noted that both grid and Gaussi an models can be configured to estim ate particle removal by surf ace cover, b u t that m a ny of the param e ters are empirical and there is little guidance or s upporting research on how to set th e input parameters in these models for a range of particle types and surface covers. He also noted th at grid m odels ignore all removal proce sses in the g r id cell into which they are first em itted, so unless

3the grid size is very sm all (100 to 1000 tim es s m aller than current ly used in regional modeling), they would not be sensitive to re m oval on the scales (10’s to 100’s of m e ters)
discussed in this paper (Irwin 2003).

The above discussion suggests that any rem ova l that m a y occur near the source (on a
scale of 10’s to100’s of m e ters) is beyond the cap ability of current grid m odels, which are intended for use in regional scale analyses, as discussed below. A m e thod is needed to adjust g r oun d level em issions of f

ugitive dus t when they a r e used to supp ort ana l yses on a scale larger than 10’s to 100’s of meters. This paper describes a m e thod to adjust the

em issions inventory used in such larger scal e an alyses as a way to compensate for the model’s inability to treat re m oval by surface cov e r near the em ission source. Note that the adjustm e nt of em issions inventories woul d be unnecessary if very small grids were used and if the appropriate rem oval m echanis m s were inco rp orated explicitly into the s e models. However, use of grid m odel s in this way would be well beyond current

com puter capabilities. Thus, the method desc ribed in this paper m a y be useful for the foreseeable future, until models are modified / developed to treat near-so u rce particle

rem oval by surface cover.

Conceptual Model: Near Source Capture (NSC) of Dust Emissions b y Surface
Cover As an extension of the work begun by DRI and the W R AP Expert Panel, Cowherd and Pace (2002) suggest th e use of a "lim iting cas es " conceptual model as a way to bound the
dust rem oval poten tial b y surfaces n ear th e sour ce of e m issio n s. An unpaved road in the
forest would repres ent o n e extrem e or lim iting ca se whereby m o st, if not essentially all of the road dust would be captured within the vegetation canopy. At the other extrem e or lim it, road em issions in barren areas of the ar id southwest would be subject to virtually no capture o r rem oval due to vegetation. Ot her surface characteristics wo uld fall between
these lim its. Cowherd and Pace refer to the fractio n of a source' s m a ss em issions captured by the vegetation (or other surface obstructions) as the "Captu re Fraction (C F)" where 0 <= CF <= 1, where 0 is a barren landscap e and 1.0 is within a dense forest. T h ey adapt the term "Transportabl e F r action" (TF) from the DRI Workshop and use it to describe those particles rem a ining airborne and availab l e for transport away from the vicinity of the source, afte r localized rem oval has occu rred (Watson and Chow 2000, Cowherd and Pace 2002).

(1) Transportable Fraction (TF) = TE /SE = {SE – [SE * CF]} /SE
Where:

Transportable Em issions (TE) =

Source Em issions (SE) – Locally Captured Em issions, and

Locally Captured Em issions = Source Em issions (SE) * CF

4Figure 1 illu stra tes the c oncep tual model for near source particle rem oval by vegetation and structures. In this simple m odel, cap ture is assum e d to increase as the density, leafiness and height of the vege tation increases. Urban areas are cons idered to be s i milar

to m i xed surroundings, on average. This m odel does not include any enhanced deposition that m i ght occur due to gravitationa l, therm a l or electros t atic f o rces. Also note that the exact relationship betw een captu re and the nature of the surroundings cannot be known without further testing.

0 .2 1. 0

.4 .6 .8

F i g u r e 1

. C o n cep t u al M o d e l – N ea r S o u r ce C a p t u r e ( N S C ) o f D u st
P a r t ic le s b y S u r f a c e C o v e r B a rre nFraction of ParticlesRemovedLocally(CF)

For e s t M i x e d S u r r o u n d i ngs < < I n c r eased C a p t u r e b y S u r r o u n d i n g s < < T a l l

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Recent and Ongoing Work to Evaluate Near Source Capture (NSC) by Surface
Cover
Field work was conducted for the Western S t ates Air Resources (W ESTAR) Council by a

team of scientists inc l ud ing Dr. Vic Etyem ezian at the Des e rt Research In stitu te (DRI)

. The effect of vegetation and structures on nearby unpaved road em issions was
docum ented in this report. The report supports the Countess findings, noting a wide range of downwind removal rates dependi ng on surface conditions. The DRI results
showed little rem oval in daytim e tests in a sparse, barren environm ent, but a nighttim e

rem oval rate of 85 percent was found at a distance of 95 m e ters downwind when

structures w e re present b e tween the road and the sam p ling apparatus (Etyem ezian 2003a).

A field study conducted by the Midw est Re search Institute (MRI) for the U.S. Departm e nt of Defense (DOD) m eas ured th e effect of groundcover on particle rem oval near an unpaved road. Initial tests w e re done over an open field 20 m e ters wide. In this test, the pa rticle s were d e pleted m i nim a lly as they passed over the field, but the depletion was about 57 percent when a bank of cedar trees was added downwind about 8 m e ters from the unpaved road. The am ount of par ticle depletion was com p arable for both
PM2.5 and PM10 over these dis t ances and tes t conditions (C owherd and Gebhart 200 3).

5The data f r o m these tes t s is lim ited a nd m o re testing is needed to im prove the confidence in the results. However, substantial near s ource removal of the partic les is apparent, even during the daytim e for particles passing ove r an open field. Cowh erd suggested that
other factors m a y enhance the deposition proces s, even over flat surfaces v e ry near the source. However, Etyem ezian saw no appare nt effect of depositi on over barren land.

Note that the effect of atmo spheric stability on C F should also be considered in future work to refine the NSC model. The CF would likely be reduced under unstable

atm o spheric conditions, which can cause the p l u m e to rise above the earth’s su rface more quickly. Conversely, the rem oval due to capture could be even higher under very stable conditions such as were present during the nighttim e test around buildings (Etyem ezian 2003a). In general, one would expect the ro le of atm o spheric stability in near source particle removal to be le ss im portant when vegetation or stru ctures are tall and/o rare located near the dust source (Etyem ezian 2003b).

Figure 2 co mpares the results of th e MRI a nd DRI field studies with the conceptual model in Figure 1. Test results from the two field studies were adde d to the schem a tic of the conceptual m odel based on descriptions of land cover between the source and the test
instrum e nts. The NSC c onceptual model s hows reasonable agreem ent with thes e field tests and thus, it appears to provide a useful fram e w ork for m a king prelim inary estim a tes of CF based on local land cover characteristics.

0 .2 !. 0

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Figure 2. Compa r is on of T e s t da ta w i th NSC Conc ep tu a l M o d e l B a rre nFractionofParticles Removed Locally(CF)

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Default Rec o mmendations for CF
Estim ation of values of CF for specific ge ographic areas requires use of a land cover dataset such as the Biogenic Em ission La nd cover Database (BELD). BELD is a

6com p endium of surface cover (m ainly vegeta tio n) characteristics u s ed by the Biogenic
Em ission Inventory System (BEIS) biogenic em ission m odel (Birth & Geron 1995). It contains data on several hundred species of vegetation at a 1 km cell size.

In the analysis presen ted in this paper, the land cove r described in BELD was grouped into five cover type groups, e.g., barren & wa ter; agricultural; grasse s, scrub and sparsely wooded; urban; and forested. The estim ated ranges and recommended values for CF are given in Table 1 along w ith the average vege tation heights assum e d in BELD. (Urban

structures w e re assum e d to range from 5 to over 50 m e ters). Ranges for the CF for each cover type are based on field work and obs ervations available from W a tson and Chow, Raupach, Etyem ezian and Cowherd, the height of the ground cover relative to the plum e and seasonal changes in the cover ch aracter istics. The ranges confor m to the linear conceptua l model in Fig u re 1 in that the CF is ass u m e d to incr ease linea r ly with the seasonal p r esence, heigh t and dens ity of the surface cover. T h e recomm ended CF values assigned to Barren and Water (0) and Forest (1 ) are chosen to be consistent with the
lim its (extrem e values) in the conceptual m odel, although as noted, the values could in fact be less than 1 for forest and greater th an 0 for water & barren. The m i d-points of the
estim ated ranges are us ed as the reco mmended CF for the other land cover types. The ranges and recomm ende d CF values should be considered a first approxim a tion and
further refinem e nt is welcom ed.

Note that the CF’s in Tabl e 1 are only generalized defau lts and should be modified by
local d a ta o r as further research b eco m e s ava ilable. Also, the estim ated CF’s herein are
believ e d to be too high f o r windblown dust ev en ts because th e wind’s tu rbulence will usually lift particles higher m o re quickly, and the opportunity for vegetative rem oval is likely reduced.

La nd Co v e r Ty pe Av e r ag e He ight

(m) Re c o mme nded CF (% ) Estimated CF Range (% ) C O M M E N T

Fore s t 18-2 0 100 % 80 to 100 % Forest ed area s w i ll capture du s t effec t i v el y

Urba n 5 – 50+ 50 % 25 to 75 % Struct ures are i n tersp e rsed w i t h op en

areas

Scrub, Sparsely
Woo d e d & Grass es 1 – 2 25 % 10 to 40 % Portio n of plu m e is bel o w sparse

v e g e ta tio n

A g ricu ltu r al 1 - 2 25 % 10 to 40 % Portio n of plu m e is bel o w crop (seaso nall y )

Barr en / W a ter 0 0 % 0 to 10 % Impe dime nt -fre e surfac es are i n eff ecti v e to ca ptur e du st

Table 1. Recommended CF ( % ) f o r Five Land Cover Ty pes

Method to Estimate th e Transportable Fraction in Specific Geog raphic Areas
The fraction of land area assigned to each land co ver type in each US County was
obtained from the BELD dataset. The county average transportable fraction was
estim ated by com b ining the CF’s in Table 1 with the corresponding fractional surface

7cover in each county an d com puting a weighted average CF for each cou n ty. The TF for each county is then estimated using equation 1 ab ove. The results are sho w n in Figure 3.
Note that th ese sam e surface cover d a ta are available in the B ELD dataset at a 1 km
resolu tion d e tail, and th e accuracy o f the m e thod could be im proved by u s ing grid
instead of county-weighted CF’s.

Figure 3 shows how the TF varies by county across the US, depending on the variation in surface cover. The differences are ap parent acros s the heav ily forested areas in th e southeas t an d the Pacific NW , the arid areas of th e Southwest, the agricultural
breadbaskets of the Central US and the San Jo aquin Valley in CA. Note that nationally, the county average TF ranges from 0 to 0.92. The TF averages approxim a tely 0.49

across a ll co unties in the US, which is less of a reduction in dust em issions than was
realized in the old “div ide-by-four: appro ach. R ecent an alysis by Pace (2005) suggests
that add i tion al redu ction in PM2.5 fugitiv e dust em issions m a y occur when the EPA

com p letes th eir investigation of apparent er rors in the m u ltiplier used to derive PM2.5

em ission factors from old PM10 em i ssion factor field m easurem ents.

The county average TF’s in Figure 3 represent the first attem p t to apply the conceptual
model to estim a te how dust rem oval by gr ound level airflow obstructions m i ght vary across the US; they will be revised as m o re infor m ation becom e s available. The transpo r tab l e fraction co ncept can b e exte nded to finer spatial resolution using an

em issions processor su ch as SMOKE (Pace a nd C o wherd 2003). In fact, the W R AP has
estim ated the TF at a 2 km resolution in support of som e of th eir analyses (Mansell 2005). In Figure 3, the county-level TF is di splayed in five ranges, each contain i ng an equal num ber of counties.

A prelim inary estim a te of the county-level TF was provided to the W R AP by OAQPS for use with their unpaved road dust em issions i nventory. Countess recently applied the NSC concept to m odeling in the San Joaquin Valley. He used the m e thod posed by Pace and Cowherd to develop county specific TF ’s based on weighted average land use and ground cover inform ation for the SJV counties. He found that use of those TF’s resulted in adjus t ed em ission estim a tes that agree well with am bient m easurem ents in th ese S J V counties (Countess 2003).

8Figure 3. Geographic Variability of the Transportable F r action

Recommen d ations and Limita tion s for Use of the Near Source Capture Adjustment Based on the discussion above, it is recomm ende d that the NSC adjustm e nt be applied to

em ission estim a tes for paved roads, unpaved ro ads, construction, tilling and quarrying in grid m odel analyses, until near-source particle removal m echanism s are incorporated into
the m odels (U. S. EPA 2004). EPA has applied the NSC adjustm e nt in regional m odeling applications and county-level TF adjustm e nt capability has been incorporated into the
SMOKE e m issions processor. An important co nsideration in the application of the N S C concept is th e scale rep r esented by th e land c over data. Land cove r in ter a cts with the source plum es over a scale of several hundred m e ters from the em ission point. Thus,
land cover data will be much m o re represen tative if it is obtained on a 1 km grid for

exam ple instead of the county-level as discus sed herein. This is quite practical to do when one is using em ission processors to prepare em issions for grid-based modeling
since the BELD dataset is available at a 1 km resolution. However, county-level land
cover data m a y be useful to adjust regi onal and county-level em ission inventories for summary reporting and m a y be useful for use wi th grid m odels if finer resolution isn’t
available.

Note that the NSC adjustm e nt should NOT be used to adjust em ission estim ates (e.g.,

perm it application s ) whe r e loc a l sc ale im p acts are im portant. Also, the adjustm e nt should not be applied to em issions input to Gaussian m odels. For Gaussian m odel applications, one should adjust the appropria te input param e ters contained in these
models to account for near source dust rem ova l. The NSC a d justm e nt is not applicable
to elev ated e m issions of f

ugitive dus t such as m a teria l transf er

points, dus t genera ted b y wind erosion or low level em issions of buoyant plum es such as open fires or vehicle exhaust since in such cases, the particles ar e assum e d to be above or rapidly rise above

9the heigh t of the surface cover. Also, the ad ju stment doesn’t currently attempt to acco unt for enhanced deposition on the ground due to th erm a l, electrostatic or in ertial forces.

Future Work Many refinem e nts have been m a de to the dust em issions estim a tion and air quality modeling methodologies over the years. Howeve r, significant issues rem a in and much
work is still needed:

1) Im prove the em ission estim ation algorit hm s, such as correcting (reducing) the

em issions for lower vehicle speeds and re-es tim ating the relationship of PM2.5 to PM10; 2) Im prove activity data, such as vehicle m iles traveled (VMT), silt content and soil moisture on unpaved roads, surface loading on p a ved roads and soil cond itions du ring agricultural tilling operations and windblown dust events; 3) Investigate ways to redu ce reliance on such difficult to obtain activity data as silt content, surface load ing and soil con d itions ; 4) Im prove both the physical and empirical understanding of the near-source enhanced deposition processes (e.g., therm a l and electros tatic forces, ag glom eration ) ; 5) Com p are and critically review the various m odels for the transm ission and rem oval of suspended p a rticles by d i fferent obstructions and surface cov e r and refine the NSC

m e thodology accord ingly; 6) Incorpo r ate the effect of atm o spheric s t ab ility into the concept of the capture fraction (CF) concep t; 7) Extend the NSC m e thodology to windblown dust m odels to incorporate rem oval by NSC; 8) More guidance is needed on the specificati on of specialized input param e ters required by plum e models; 9) Continue to im prove the rem oval m echanis m s in both grid and Gaussian m odels.

Conclusions Our understanding of factors affecting partic le rem oval near ground level fugitive du st sources has improved because of work be gun at the EPA-sponsored Fugitive Dust Workshop held at DRI. Models are lim ited in th eir ab ility to fully accou n t for near

source rem o val of particles for a variety of physical and practical reasons, and this
lim itation is a m a jor reas on f o r the disparity betw een m odeled and m onitored estim a tes of
f ugitive dus t. The recog n ition that v e geta tion ca ptures som e of this dust has led to a useful, albeit em erging m e thodology to accoun t for the near source rem o val of particles in regional and urban scale analyses. This m e thod is an im provem e nt upon the national divide-by-four adjustm e nt that has been used for about ten years. It m a y be applied in regional scale analyses where fugitive dust is em itted from paved and unpaved roads, construction, agricultural tilling, quarrying and ear thm oving. Note that as research in this

area evo l ves , other app r o aches or ass u m p tions may be deem ed m o re appr opria te so it will be prudent to review the NSC adjustm e nt m e thodology as new studies are published. Also, local k nowledge ab out surface cover shoul d be incorpo r ated when av ailab l e.

10A c know ledgements The author is grateful to Drs. John Watson, Judith Chow, Richard Countess, John Irwin, Frank Binkowski, Dale Gillette, Chatten Co wherd, Vican Ety e m ezian, Mr. Philip Lorang
and Mr. W illiam Barnard for their research, in sig h t and thoug htful consid eration of th e issues as this conceptual m odel evolved.

References Birth, T.L. and C.D. Ger on. Users Guide to the P e rsonal Com puter Version of the Biogenic Em issions Inventory System (PC- BEIS ) Version 2.0, Prepared by CRC for US EPA/AEERL, RTP, NC March 1995. http://www.cm ascenter.org/

Cionco, R., Ar m y Research Lab, Personal Co mmunication to T. G. Pace, June 25, 2002

Countess, Richard, W

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Dust Em issions Applicable for Regiona l Air Quality Modeling”, EPA Em issions Inventory C onference, Denver CO 2001. http://www.epa.gov/ttn/chief/c onference/ei10/fugdust/countess.pdf http://www.wrapair.org/foru m s /dej f/docum ents/FugativeDustFinal.doc

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, 2005.

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2000. http://www.epa.gov/ttn/chief /efdocs/fugitivedust.pdf

Note: The 0 80305 revisi on made min o r change s in the values assigned for the capture
fractions for several land cover types. Over all effect was to reduce the county- l evel transport fr action (nati o nal gr and average) from 0. 49 to 0. 46 . However, some individ u al county T F ’s changed by as much as +/- 20%.

The original docu ment, pu blished Augu st 3, 20 0 3 , was titled “Conceptual Model to Adjust Fugitive Dus t Emissions to Acc o unt fo r Near Sour ce Part ic le R e moval in Gri d Model Applications.”

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