# Locating and QuantifyingWind Blown Dust PM-10

Emissions at Owens Lakeo California Paper # 69487 Duane Ono Ellen Hardebeck Scott Weaver Billy Cox Nikolai Barbieri William Stanley Great Basin Unified Air PollutionContol District, 157Shod St.,Bishop,CA 935L4 Ken Richmond MFG, Inc.,1920336ftAve., W, Suite 101, Lynnwood,

WA 98036-5772 Dale Gillette National Oceanic

andAhnospheric

Administation, US EPA Mail fhop 81, Research Triangle ParlqNC 27711 

### ABSTRACT Wind blown dust at OwensLake, Califomia has caused the highestPM-10 concentrations measured

in the United States. Twenty-four hour average concentrations

have exceeded 12,000 pglm3,which is morethan 75 times higher thanthe National Ambient Air Quality Standard (NAAQS). A monitoring program, known asthe Dust Identification (Dust ID) Program, was started

in the year 2000 to identiff dust source areas that caused

or contributed

to NAAQS violations. Based on previous research

that found that PM-10 emissions

were proportionalto sand flu:q a network of sand

flux monitors were installed at OwensLake. Passive and electronicsand

flux monitorswere placedon 135 sq. krn of the lake bed at a I km spacing to measure

the hourly sand

flux at eachsite for 30 months. This sand

flux informationwas used with the CALpLJFF

6sdsling system

to model pM10 impactsat shoreline

receptorso

including 6 PM-10 monitor sites. Hourly PM-10 emissions

from eachsquare

kilometer were estimated by the equation,

PM-|0 : Kf * q, where4 is the sand

flux measured

at 15 cm above

the surface,andK1 (K-factor) is the non-dimensional

ratio of the PM-10 emission flux to the sand

flux at 15 cm. Initial model runs useda constant KTvalue. After the initial model run, hourly variationsof Ky were evaluated

by comparing

the model predictions

to the observed

concenffations

at the PM-10 monitor sites. AverageK,r values were found to change

spatially andtemporally at Owens Lake. These

changes corresponded

to different lake bed soil texturesand to seasonal changes in the surface

erodibility. Wind tunnel testsat OwensLake independently

confirrred these

seasonal and spatialchanges

in K1.

AWMA Owens Lake Emissions paper-E.docThe resultsof the Dust ID Program showed that sand flux measurements

can be used with K-factors to quantiff PM-10 emissions for wind blown dust areas. Data collected from the Dust ID network provided valuableinfomration on the location, frequency and intensityof PM-10 dust source areas at Owens Lake. During the study period, the peak daily PM-10 emissions

from Owens

Lakewereestimated

tobe7,2l0 tons on May 2, 2001, and the annualemissions were estimated

to be 79,400

tons from July 2000 tbrough June 2001. 

### INTRODUCTION The Great Basin Unified Air Pollution Conhol Dishict hasbeenstudying air pollution caused by wind blown dust from Owens Lake for over 20 years. This paperdescribes the methodology

and resultsof a project at OwensLake, Califomia to quantiff PM-10 emissions

from different areas

of the exposed

lakebed. The OwensLake Dust Identification (Dust ID) Program providesreal-time dataon saltation flux, PM-10 concentations and meteorological parameters

to estimate

PM-10 emissions

and model ambientimpactsof dust events. The progran also utilizes observers and video cameras to record dust producing areas and plume directions. These observations

are used to help corroborate

the modeling effort. The Dust ID Programuses a two-part methodolory to model PM-10 emissions and impacts. The first part of the Dust ID Progra:nis to estimate PM-10 emissions

using sand flux measurements

andthe PM-10 to sand

flux ratio. This ratio, known asthe Kfactor, was determined

for different periodsand areas of the lakebed.

The second

part of the Dust ID Programuses a dispersion

model to predict the dust impacts and determine which areas

causied or contributed

to violations of the NAAQS. A companion paperby Richmon4 et al. describes the CALPLTFF

slsdsling resultsin more detail and how the Dust ID information was used to select areas for conftol measure

imFlementation.r This paperdescribes

the methodology

to estimate

PM-10 emissions

dueto wind blown dust at OwensLake andthe resultsfor a 3O-month period. 

### BACKGROTJI\D The dried bed of OwensLake in Inyo County, Califomia is the largest single source

of particulatematter pollution in the United States.' The lakebed coversan area approximately110 square miles (285 sq. km) and is a natural salinelake at the terminus of the Owens River. When the OwensRiver was divertedby the City of Los Angeles into an aqueduct

in 1913,it caused

the lake to become

virtually dry by 1928. A small permanent

brine pool is what remains at the lowest part of the basin. It is surrounded by exposed, dry alkali soils. Wind blown dust from the exposed lakebed can cause

24-how average PM-10 concentrations

to exceed

12,000 pglm' at the historicshoreline - more than 75 times higher than the federal PM-10 standard. The air quality monitor site at the Town of Keeler averaged 19 violations per yeax

of the federal24-hour PM-10 standard

AWMA Owens [-ake Emissions paper-E.doc(150 pgim3) from 1987-1995.

Dust storms

often affect

the health

andwelfare

of people livine *itni" 50 miles (80 km) of the lake.3 In 1993, the US EnvironmentalProtection Agency (US EPA) designated the southem Owens Valley as a 'Serious' PM-10 nonattainment area. As a result, a State Implementation

Plan (SIP) was developed with a contol sfrategy

that would bring the area into attainment

by December

31,2006. The SIP established interim requirements for implementing

dust controlson 16.5square miles (43 sq. km) of the lakebed by the end of 2003.4 Large-scale implementation

of shallow flooding was started in 2001 and rnanaged vegetation

ln2002. The City of Los Angeles is expected

to meet the 16.5 square mile target by the end of 2003. Through the Dust ID Program additional areas will be identified for control measure implementation

to bring the areainto attainment with the PM-10 NAAQS by the statutory deadline of December

3I,2006. 

### THT'6py Previous to the Dust ID Program,PM-10 emissions from wind erosion

were estimated using equations

for open area

wind erosionand agriculture found in US EPA Guidance documents,

suchas AP42. These

emission estimation

methods are based

on knowing the tbreshold

friction velocity of a surface, which is the wind speed

that will initiate wind erosion. Thoseestimationmethods also assume

that a fixed portion of the dust is composed

of PM-10. Wind tunnel measurements at OwensLake showed

that the threshold

friction velocity and the proportion of PM-10 in the eroding soil will change with different surface

conditions,suchas soil moisturecontent,soil texture, and soil binding properties.5'6

Aeolian experts

havedeveloped

more complex equations to estimate PM-10 emissions. The Shao, et al. expression

for the PM-IO_vertical

flux (F') of suspended

particlesproduced

by the impact of saltating particles is ' Equation 1.

l'o) 

# F.=T*^lf)or(W\ Where: Y = binding energy y = a constant md = mass

per particle size g: acceleration

of gravity Q:total horizontal

saltation flux Va :hortzontalvelocity of a saltating particle

AWMA Owens Lake Emissions paper-E'doc
- 3

_Fo ( ma\



# s =' lv) z* : friction velocity. Both y andg are constants and the function l(ut", has been shownby Owen to be almost constant.s

Therefore,

the ratio Fo/Q maybe expressed as Equation 2.



### Where: ( tru\ 

# F =T rrl;) l' can be considered

to be atnost constant. At OwensLake the mass per particle size,

m4 may have similar values for areas with similar surface

soil textures

andthe binding energy, which is relatedto the aggregate strength, appears to change

seasonally

with precipitation,

temperature

and surface

soil chemistry. For certainareas and periods,the ratto of Fo/QwouId, be nearly constant if the soil particles

were of similar mass

and size in that area

andthe binding strength was similar for tbat period in the sa:ne area. Therefore,

the ratio Fo/Q.,could be usedto characteizethe ratio of the vertical PM-10 flux to the horizontal sand flux for a certainarea

and period. Shao and Raupauch

found that Q is distributedaccording to Equation3, with the flux decreasing

rapidly asyou move higher above the surface.

Ten sitesat Owens Lake were instnrmented

to collectsand

flux dataatthree

different heights (15,25, and 50 cm) and the datawere usedto compare the sand flux at 15 cm above

the surface

to the total sand flux. The multi-height data were fitted to Shao

and Raupach's

empirical equation

for saltation mass flux profiles,e Equation 3. m(z) : c exp[az+b/J where: m(z) : mass

asi a function of heigh! z, anda, b, andc are constants. Gillette, et al. fit the multi-height measurements to the equation

and integrated

the mass flrx profile to determine the total sand

flux from the surface

to I meter,which includes almostall the sand

flux. Equation 4.

llm Q=lomdz

AWMA Owens Lake Emissions paper-E'docGillettefoundthat for Owens Lake the sand

flux at 15 cm (14.5

to 15.5 cm) is proportional to the total sand flux andthat a single sand flux measurement

at 15 cm can be usedas a surogate for the total sand flux measurement.

The mean

value of the sand flux at 15 cm to the total sand flux was 2.4 rnl with a standard

deviation of 0.2 m-l.10 In this study, F" /Q is converted to the ratio of PM-10 to saltation flux at 15 cm and is referredto asthe K-factor (r<). The value for Fo/Qhas the dimensiornper unit length, whereas,

Kyis a dimensionles_s

value. Previous

researchers

have reported

F, /Q values for other wind erosionareas.t For comparison purposes,

Fo/Q [m-tJ, canbe relatedto .ft] using the empirically derived ratio of the sand flux at 15 cm to the total sand flux, Equation 5. F" 

# o =2.4 x Kr
After determining

the K-factor for different areas and periods at Owens Lake, PM-10 emission flux rates(F,) can be estimated by knowing the sand

flux rate for a given area using Equation 6. Fa: Kyx

q Where: q =horvonta! sand

flux rateat 15 cm above the surface

lglcm2/h4. Since K,risa dimensionless

value the units for Foare

the same asfor q.For modeling using CALPUFF, Fo is converted to g/s for eachsquare

kilometer. The sand

flux rate is directly measured

during dust storms

with instnrments

centered in eachsquare

kilometer ofa 135squarekilometermonitoringnetwork. Directfieldmeasurementof sandflux ratesin the Dust ID Programis a key difference from other PM-10 estimationmethods for wind blown dust. Other methods use differences

between the wind speed

and the threshold

friction velocity to estimate the saltation

flux as opposed

to the Dust ID method of directly measuring

the saltation

(or sand flut) flux in real-time. K-factors can be infened from the CALPUFF model by using hourly sand flux as a surrogate

for PM-10 emissions.ll ModeledPM-10 predictions canthenbe compared

to monitored concentations at 6 PM-10 monitor sites to determine

the K-factor that would correctly predict the monitored concenfration for eachhour. After selecting

appropriate

K-factors for different areas and periods,CALPUFF can be run with the appropriate hourly emissions

for each square kilometer cell using Equation 6. The PM-10 source

contributions

from eachcell to shoreline receptors can then be

AWMA Owens Lake Emissions paper-E.docFigure 1. Owens Lake Dust ID monitoring network

Legend

. Sand Flur O Met ^ PM10

evaluated

to detemrine

which dust producing areas must be confrolled to demonshate attainment

with the PM-10 NAAQS. 

### METIIODOLOGY A network of sand

flux samplerso

met towers and continuous PM-10 monitors were operated at Owens Lake for 30 monthsto collect dataon wind erosion. Figure I showsa map of Owens Lake with the location of the Dust ID instumentation. To help verify the location of dust source

areas, time-lapse

video cameras

were installedat 3 sites

to continuouslyrecord dust events during daylight hours and threeobservers mapped dust source areas and plumes during the stormson regularworkdays. In addition, the erosion boundaries

of somesource

areas were mapped

with the aid of a Global Positioning System (GPS) aftsr a storm. The GPS dataandthe visual observer's rnaps were put into a Geographic

Information System

(GIS) database.

Slong with the sand

flux datato corroborate

the locationof dustproducing areas.''

AWMA Oweng Lake Emissions paper-E.docFigure 2. Two Sensits are seen suspended

above the ground and a CSC is located to the left at this Owens Lake test site used to compare the performance of different saltation measurement insfi'rments.



### Sensitsmand Cox SandCatchers CoJocated Sensitsru

and Cox Sand

Calchers (CSC's) were used

to measure

hourly sand flux mtes at 135 locations as shown in Figure 1. The instruments were placedwith their seluior or inlet positioned 15 cm above the surface. Sensits

measure the kinetic energyor the particle countsof sand-sized particles asthey saltate

across the surface. Due to differences

in the electronicresporse of individual Sensits,

they must be co-located

with passive sand flux measurement

devices to calibrate

the electronicoutput andto determine the hourly sand

flux.r3 Figure 2 shows

two Sensits

suspended

above the ground and a CSC in the ground to the left. The photo was taken at a site that was usedto test the accuracy of Sensits

and CSC's before the Dust ID Program began. The battery powered Sensits are augmented

with a solar chargingsystem. A data logger recordshourly Sensit dataduring inactive periodsand switches to 5-minute dataduring active erosionperiods. CSC's are passive

instruments

that are usedto collect sand-sized particlesthat are blown across the surface

during a dust event. These instruments

were designed

and built by the Great Basin Unified Air Pollution Control District as a reliable instnrment that could withstandthe harshconditionsat OwensLake. CSC's haveno moving parts and can collect sand

for a month at Owens Lake without overloading the collector. A diagramof the CSC is shown in Figure 3. Not shown in the diagram are an internal sampling tube and a height adjustnent sleeve. The intemal sampling tube can be removedfrom the PVC casing

to measure

the sand catch sample.

The length of the sampling tubesand sasings are adjusted

to accommodate

the amountof erosionin eacharea andto avoid overloading

the CSC's. The CSC length ranges from about 2to 4 feet. Because the

AWMA Owens Lake Emissions paper-E.docFigure 3. Diagram of the Cox Sand Catcher (CSC) used to measure sand flux at Owens Lake.

PVC casingis buried in the groun4 an adjusfrnent sleeve is used to keepthe inlet heigbt at 15 cm to compensate

for surface

erosionand deposition. Before the start of the study,the CSC sampler was calibrated

against the Big Spring Number Eight (BSNE) samplerused by the US Deparbnent

of Agriculture and othersfor wind erosion

studies.la

Figure 4 compares

the CsCcollectedsand

mass to the BSNE (r2 :0.97). This comparison was also usedto detemrine the apparent

size of the inlet opening of the CSC to calculate

the sand flux. BSNE's have a defined inlet areasizethat can be used

with the collectedmass

to calculate

the sand flux fmass/area].

The CSC's circular design,however,doesnot have an obvious inlet area sizefor a flux calculation. By comparing

the CSC sand

catchto the BSNE fltur, the CSC was found to have an apparent inlet size of L.435 cnf

. During the same testsit was found that the CSC's had better precision thanthe BSNE's. The precision for 3 co-located

CSC'swas* 3%, while the BSNE's hada + 8% precision. An example

of the linearity between

the CSC collectedsand

mass andthe particle count datafor a Sensit

is shown in Figure 5. The Sensit particle count datawas normalized for the tbree Sensits

by dividing the total particle count for eachSensit to the average

of 3 CSC's summed

for all the nrns. This type of comparison is routinely used

to checkthe calibrationof individual Sensits every month, however,

the calibrationcurve that is generated

is not normally used

to determine

the hourly sand

flux. Because

the electronic

AWMA Owens Lake Emissions paper-E.doc
- E

_ Inrpar:torRod--l:*"" l- l't *



### Rernovable

Clapl. 

## R 

# l'; =;.;; {

,/

.a 

# v

t Figure 4. Test resultsfor CSC's compared to the ayerage

sand catch for 3 BSI\E's. Figure 5. Test results for Sensit readingscompared to 3 CSC's.

0 100 200 300 400 500 600 Average CoxSand Catcher (grams) 700 -g € 600 d E 500 € ! E400 

## .H E.oo $ 2oo E too z 0 700 th E 600 E .9 soo o € 4oo (s o goo E c 

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### 6r 0 100 200 300400 500 600 700800 900 Average BSNE Gatch (grams)

Sensit resporurc and calibration can drift, the Sensits were used in combination with CSC's to determine

the hourly sand

flux. This combination

takesadvantage

of the good precisionand accuracy

of the CSC sand

catchdat4 andthe ability of Sensits to timeresolve the sand flux for each

hour of the CSC sampling period. In this way, the sum of the hourly sand

catches alwaysmatches

the CSC sand

catchfor each

monthly sampling period and it minimizes the error in the hor:rly sand flux. Samples

from most of the CSC's were collectedoncea month, and l0 intensivesites were collected after every dust storm. 

### PM-10 & MeteorologicalData Hourly PM-10 data

werecollected

at 6 sites around Owens Lake usingTEOM PM-10 monitors (Figure 1). These monitors were co-located with filter-basedPM-10 monitors including: PM-10 Partisols and Wedding andAndersen Hi-Vol Size-Selective

Inlet PM10 monitors. A previous study by Ono, et al. showed that the TEOM PM-10 monitors were well suitedto measure wind blown dust at OwensLake andto provide hourly PM10 dab.t5 Meteorologicalpara:neters were me:lsured

at 13 siteson and around Owens Lake. A wind profiler was operated at Dirty Socks

and later movedto the Mill Site to collect wind dataaloft to supplement he surface

wind observations.

The wind profiler also included a Radio Acoustic Sounding System (RASS) for the collection of vertical virfual temperahrre

profiles.

AWMA Owens Lake Emissions paper-E.doc

### K-factors

K-factors were determined through a combination

of dispersion

modeling, sand

flux rates and monitored PM-10 concentrations. Dispersion

modeling was performedusing the CALPUFF modeling system, whichutilized meteorologicaldata from thirteen lO-meter towersand an Upper Air Wind Profiler to generate wind fields using the CALMET model.l I More information 616sd6ling is provided in a companion paperby Richmond et al.r CALPUFF was used to model the dust impactsat six PM-10 monitor sitesusing Equation6 with the hourly sand flux (q) from eachs_quare kilometer to generate

the hourly PM-10 emission

rates. A K-factor of 5 x 10-'was used to initially run the model andto generate

concentration

valuesthat were closeto the monitored concentrations. Hourly K-factor valueswere later adjusted in a post-processing

stepto determine

the Kfactor value that would have madethe modeledconcentration matchthe monitored concentration

at eachof the 6 PM-10 monitor sites. The initial K-factor was adjusted using Equation 7. Equation 7.
Kt= K
Where: K; = Initial K-factor (5 x 10-s) Cobs. : Observed

hourly PM-10 concentation. [pdm3] Cbor. : BackgroundPM-10 concentration (assumed

2O pglm3) C^od. : Model-predicted

hourly PM-10 concenhation. [pglm3] K-factors were calculated for every hour that had active sand flux in cells which were upwind of a PM-10 monitor. These hourly K-factors were then screened to remove hours that did not have stong source-receptor elationships

between the active source

areaand the downwind PM-10 monitor. For example, the screening

criteria excluded

hours when the edge of a dust plume may have impacted a PM-10 monitor site. Because the edgeof a dust plume has a very high concentration gradient a few degrees

eror in the plume direction could greatly affect the calculated K-factor. Ten different screening criteria were usedto find the K-factor hours that had the best source-recep,ot "1u1ie6hips. Examples

of the screening

criteria included:hourly modeled and monitored PM-10 were both greater

than 150 1tglmt,the

sand flux wasgreater

than2 {cm2lhr in at least onecell that was located

within l0 hn and +l5o upwind from a monitor site, and more than 65%o of the PM-10 contibution at a monitor site camefrom the target source area. The screening

criteria deleted

a numberof outliers and also many points that were in the expected K-factor range,

but overall resultedin reducing the K-factor variability.

AWMA Owens lake Emissions paper-E.doc

_ l0

_

(C,u. - Cw.\ 

# lc*")F'igure 6. Dust source areas are scattered around a 35 square mile (90 sq. k-) area of

the Owens Lake bed in this aerial photo of a dust storm.

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### RBST]LTS Sand Flux and ObservationMaps For the Dust ID Program, obseruers

were stationed

at 3 locations around

the lake to map the location of dust source areas and record the plume trajectories. These rulps were handdmwn every 15 minutesduring the storms. The aerial photo in Figure 6 showsan exarnple of an Owens Lake dust storrr. The dust source areas in this photo, taken around 1989, are scatlered

arounda 35 square

mile (90 sq. km) area

of the eastside of the lakebed. The Dust ID observations were supplemented

with information from timelapse video cameras.The visual observations were used

to help corroborate

the source area location information provided by the Sensits and CSC's andto veriff plume heights andtajectories for the CALMET and CALPLJFF models. Figure 7 shows the dust phrme observations

and the total sand

flux at eachsite for a large storm that started on February 6,2001and continued

for 53 hours. Although the dust plume rnaps were only drawn for 6 honrs of the storm, they show good agreement with the sand

flux measurements

that weretaken for the entire stomr.The observer mapsalso provided useful information on the location of small dust source areas that were not caugbtby the 1 km spaced sand flux

AWMAOwenslakeEmissionspaper-E.doc -It
-

il:!;:'lii l++Figure 7. The daytime observed dust plumes correspond very closely to the total sand catch massfor each CSC/Sensit site as demonstrated by this wind blown dust event on February G8,2001.


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AWMA Owens Irke Emissions paper-E'doc35 30 25

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## E,'u 10 Figure 8. Hourly K-factors were compiled for four different areas of the lakebed and evaluated for temporal variations. K-factors are shown for the South Area.

utr ay 9u I Olancha a ShellCu o Flat Roc --l-Stnm A, I trtr 

### E o 

# ,'AJ E) 

# E' IH -{

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# ru Ei Itr t E I 

### H Apr-00 Apr-O'l Date Apr42

network. Sensits

recorded sand flux every 5 minutesthroughout the dust stormsand provided erosiondatawhen plumescould not be seen, suchas at night and when the plume itself obscured

the observer's

view of erosion

on the surface. 

### fsmporal and Spatial K-factors About 1,000hours of screened datawere usedto generate temporal and spatial K-factors. Figure 8 shows

the hourly K-factors for the SouthArea of the lakebed. The resultsshow scatter in the hourly values,

but a fairly constant

average K-factor for eachstomr during certainperiods of the year. The storm average for the South

Are4 aswell as other areas usually increases

during the winter and early spring. This period corresponds to the formation of an efflorescent salt on the surface

that forms a very powdery and loose surface. Efflorescent

saltsform annuallyat OwensLake with increased precipitationand cold temperatures. In addition to the South

Area, three other areas

of the lakebed

were identified for the spatialK-factor sets: the Keeler Dunes, the North Area, andthe Cenhal Area. The boundaries

of the four areas,

which are shownon the map in Figure 1, were delineated by a soil surveyof the surface soil textures. All four areas showed temporal K-factor trendso aswell as some

differences

that may be attributed

to different soil textures. Data plots similar to Figure 8 for the South Area were generated

to determine

spatial and temporal K-factors for all four areas. Table I shows a srunmary

of the temporaland spatial K' factorsthat were generated from the screened

K-factor data. The 7iVoK-factor set is usedbecause

it was found to provide the bestmodel performance for the high PM-10 duyr.t 0++ Jan-00

AWMA Owens Lake Emissions paper-E.docTable 1. 7S-percentile storm-average K-factors were determined to provide

spatial and temporal values to estimate hourly emissions and model ambient PM-10 impacts.


### Period K-factors (1O1

Keeler Dunes North Area Central Area South Area 

### L/U00-213101 5.1 2.1 6.6 1.9 

### 2/4/0r4/18/01 5.1 

### 2.1 26 6.7 

## 4/19/0r-rr/30/0r 5.1 

### 2.1 6.3 t.9 12/t/ol-3/8/02 20 7.6 

### 36 5.8 

### 3/9/024/18102 5.5 

## s.0 6.9 

### 9.0 4/19t02-6/30/02 5.5 

### 5.0 6.6 1.8
Figure 9. Daily PM-10 emissions were measnred

as high as7r2l0 tons on May 212001andtotal emissions for oneyear totaled 791400 tons.

a


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### PM-10 Emissions Hourly, daily, and annualPM-10 emissions can be calculated

using Equation6. Figure 9 shows the daily PM-10 emissions based on the hourly sand

flux for eachcell and the appropriate

temporal and spatialK-factor from Table 1. The highestdaily emission total duringthe program

was calculatedatT,2l0tonsof PM-10 (6,540 metric tons)on lv,{ay

2, 2001. The annual

PM-10 emissions

for Owens

Lakeweredetermined

to be 79,400

tons (72,000 metric tons)from July 2000 through June 2001. This l2-month period was used to estimate

the annual

emissions

because the full sand

flux network was in place during this period and it was not influencedby the dust contols that were implemented at the end of 2001

. Sincethe sampling

period only covers

30 monthsandthe lakebed emissions are highly variable,it is likely that peakannual and daily emissions

havebeen

higher underdifferent conditions. For exarnple the 1995emission

estimates

showed a peak day

AWMA Owens Lake Emissions paper-E.doc

_ A

_ 8000 6 7000 tr o c 6000 o tr € 5000 c! 

### fi aooo o I 30oo o- -2. 2000 6 o looo 0 +t' Jul-00of 8,346 tons (7,587

metic tons)on April 9, 1995, andannual

emissions

of 279,900

tons (254,455 metic tons) in 1995. The 1995emission estimates were based

on wind tunnel generated

emission factors.3 Currently, PM-10 emissions are on the decline asthe result of dust mitigation efforts that started in 2001. Emissions

are expected

to continuetheir decrease as more dust areas

are confrolled through 2006. 

### DISCUSSION Sand flux was found to be the most critical factor that affected the PM-10 emissions. Hourly sand

flux rates

were found to vary by more rhan a factor of 100 in active areas, while the hourly K-factor rarely varied by more than a factor of 2 or 3 from the stormaverage value. Although the sand flux rate had the strongest influence on the PM-10 emission estimates,

much of the focus of the Dust ID Progra:n was on the variability of the hourly K-factors. Ten screening criteria were used

to remove hoursthat may have had a weak source-receptor relationship. These

criteria objectively screened-out many hourly values

tbat appeared

to be outliers and significantly reduced the variability of the average temporaland spatial K-factors. One of the confirming points about the K-factor method

was the apparent

agreement

with K-factors that were calculated from different monitor sites, which were in different directionsand distances

from the target source

area. Figure 8 shows

that the hourly Kfactorsgenerated

for the South

Area were very similar when generated from Olancha, Dirly Socks,

or Shell Cut. By inspecting

dataplots suchasthe one shown in Figure 8 for eachare4 it was found that the average

K-factors were relatively constant most of the time. However, during the late winter and early spring K-factors increased by about a factor of 5 in some areas. This increase

in the K-factor appears

to correspond

to precipitation events

and cold temperatures.

The surface

change generallyappears

as a white efflorescent

powdery salt on the surface

and there is a general

breakdown

of any surface

crusts that may have existed. This change

may affect the entire lakebe{ or just parts of it. Since these periods have someof the largestdust storms,it is importantthat these changes in the surface conditionsand K-factors are definedto properly model this period. Wind tunnel tests

were perforrred by Nickling et al., and Nickling and Brown to provide comparative

information on the K-factors gsing a different measurement method than the Dust ID Program.t'o Wind funnel measurements were not taken randomly since the runs were performedon surfaces that had beenobserved

to be active erosionareas. Individual test runs using the wind tunnel showed a large rangeof K-factor valuesfor the same sites that could be more than 100times different. However, as shown in Table 2, the averages of the runs showed

similal averages

to thosemeasured

by the Dust ID methodfor the same areas and during the same

period. It is importantto note that individual wind tunnel runs measure

erosionon a 12 square

meter surface,

whereas the Dust ID method measures the average

K-factor for a minimum of I to 3 million square meters at onetime. This assumes

that the Dust ID K-factor is based on sand flux for alarge area

andtbat I to

AWMA Owens Lake Emissions paper-E'doc

_ 15

_Table 2. Comparison of temporal and spatial K-factors measured with a wind tunnel and the Dust ID method at Owens Lake. Average K-factor Dust ID Period Area Wind Tunnel 

### Dust ID 

### t/t/00 - 2/3/0r North Area 

### 2.3 x 10-" 1.8

x l0-' 

### t/t/00-213/01 Keeler Dunes 

### [.3 x 10-' 3.5

x l0-' 

### 2/4/0r - 4/t8/0t CentralArea 9.7 x 10-" 24.1

x lO'' 

### 2/4/01 - 4t18/0r SouthArea 6.6 x l0-" 5.9

x l0-" 

### ltglot - ll/30/0r CenfralArea 16.0 x 10-' 5.7

x 10-' 

### 4/t9/0r - rr/30101 SouthArea 

### 3.1 x 

### 10- 2.0

x 10-"
3 square kilometer areas

rnay be the size of the primary dust source areas during any single hour that K-factors are generated. The wind tunnel runs demonshated the large variability in small-scale wind erosionmeasurements that is common

to all surfaces,

even whenthey may appear

to be quite uniform. By averaging the results

from many runs, the avemge wind tunnel generated

K-factors became

more representative

of the value for a large erosionarea

as measured

using the Dust ID method. Measurements

of K-factors and wind erosionare expected to continueat Owens Lake for the purpose

of monitoring the implementation of control measures

and to identifr any new source

areas that rnay cause

or contribute

to NAAQS violations. The authors believe the Dust ID methodcan be applied in otherwind erosionareas andthat additional research shouldbe doneto measure K-factors and sand

flux for other places. For other areas, the extensive

monitoring perforrred for the Dust ID Projectcould be substantially reduced in size or concentrated

in smallerareas

to locatewind blown dust sources andto measure their PM-10 emissions. 

### CONCLUSION A monitoring program, known as the Dust Identification (Dust ID) Progranr, was started in the year 2000 to identifr dust source areas that caused

or contibuted to NAAQS violations. Based

on previous research

that found that PM-10 emissions were proportionalto sand

flux, a network of sand

flux monitors were installed at Owens Lake. Passive and electonic sand

flux monitorswere placedon 135sq. km of the lake bed at a I km spacing

to measure

the hourly sand

flux at eachsite for 30 months. This sand flux informationwas used

with the CALPUFF modeling system to model PM-10 impactsat shoreline receptors,

including 6 PM-10 monitor sites. Hourly PM-10 emissions from eachsquarekilometerwereestimatedbytheequation, PM-|0:Kf x q,where

4isthe sand flux measured

at 15 cm above

the surface,

and Ky(K-factor) is the non-dimensional ratio of the PM-10 emission flux to the sand

flux at 15 cm. Initial model runs used a constant Klvalue. After the initial model run, hourly variations ofK,rwere evaluated

by comparing

the model predictions

to the observed

concentations at the PM-10 monitor

AWMA Owens Lake Emissions paper-E.doc

_ t6 -sites. AverageK7 values were found to change

spatially and temporally at OwensLake. These changes corresponded

to different lake bed soil textures andto seasonal

changes in the surface

erodibility. Wind tunnel tests at OwensLake independently confirmedthese seasonal and spatialchanges

in K7. The resultsof the Dust ID Progran showed that sand flux measurements

can be used with K-factors to quantit' PM-10 emissions for wind blown dust areas. Data collected from the Dust ID network providedvaluableinformation on the location, frequency and intensity of PM-10 dust source areas at Owens Lake. During the study period, the peak daily PM-10 emissions

from Owens

Lakewereestimated

tobe7,2l0 tonson May 2, 2001, and the annualemissions were estimated

to be 79,400

tons from July 2000 through June 2001. 

### ACKNOWLEDGEMENTS The authors

gatefully acknowledge

the tremendous

job done by the Air Quality Technicians

at the Great Basin Unified APCD; Guy Davis, Chad Stein and Dan Johnson who helpedbuild the Cox Sand Catchers,

install the Sensits

and PM-10 monitors and collect the Owens Lake field data,and Mike Slates, who assembled

the GIS information, and created

many of the figures in this paper. The authors are also grateful for the wind tunneltestrng

performed

by the University of Guelph, which was led by Bill Nickling and Laura Brown.


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KEYWORDS Wind Blown Dust, PM-10 Emissions, OwensLake, CALPUFF, K-factor 6.
7.
8. 9. 10.
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