Locating and Quantifying Wind 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 Pollution Contol District, 157 Shod St., Bishop, CA 935L4

Ken Richmond

MFG, Inc.,19203 36ft Ave., W, Suite 101, Lynnwood, WA 98036-5772

Dale Gillette

National Oceanic and Ahnospheric Administation, US EPA Mail fhop 81, Research

Triangle Parlq NC 27711

ABSTRACT

Wind blown dust at Owens Lake, Califomia has caused the highest PM-10 concentrations

measured in the United States. Twenty-four hour average concentrations have exceeded

12,000 pglm3,which is more than 75 times higher than the National Ambient Air Quality

Standard (NAAQS). A monitoring program, known as the 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 proportional to sand flu:q a network of sand flux monitors were installed

at Owens Lake. Passive and electronic sand flux monitors were placed on 135 sq. krn of

the lake bed at a I km spacing to measure the hourly sand flux at each site for 30 months.

This sand flux information was used with the CALpLJFF 6sdsling system to model pM10 impacts at shoreline receptorso including 6 PM-10 monitor sites. Hourly PM-10

emissions from each square kilometer were estimated by the equation, PM-|0 : Kf * q,

where 4 is the sand flux measured at 15 cm above the surface, and K1 (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 KTvalue. After the initial model run, hourly variations of Ky

were evaluated by comparing the model predictions to the observed concenffations at the

PM-10 monitor sites. Average K,r values were found to change spatially and temporally

at Owens Lake. These changes corresponded to different lake bed soil textures and to

seasonal changes in the surface erodibility. Wind tunnel tests at Owens Lake

independently confirrred these seasonal and spatial changes in K1.

AWMA Owens Lake Emissions paper-E.doc
The results of 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 valuable infomration 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 Lake were estimated tobe7,2l0 tons on May 2,

2001, and the annual emissions were estimated to be 79,400 tons from July 2000 tbrough

June 2001.

INTRODUCTION

The Great Basin Unified Air Pollution Conhol Dishict has been studying air pollution

caused by wind blown dust from Owens Lake for over 20 years. This paper describes the

methodology and results of a project at Owens Lake, Califomia to quantiff PM-10

emissions from different areas of the exposed lakebed. The Owens Lake Dust

Identification (Dust ID) Program provides real-time data on saltation flux, PM-10

concentations and meteorological parameters to estimate PM-10 emissions and model

ambient impacts of 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 Program uses a two-part methodolory to model PM-10 emissions and

impacts. The first part of the Dust ID Progra:n is to estimate PM-10 emissions using

sand flux measurements and the PM-10 to sand flux ratio. This ratio, known as the Kfactor, was determined for different periods and areas of the lakebed. The second part of

the Dust ID Program uses a dispersion model to predict the dust impacts and determine

which areas causied or contributed to violations of the NAAQS. A companion paper by

Richmon4 et al. describes the CALPLTFF slsdsling results in more detail and how the

Dust ID information was used to select areas for conftol measure imFlementation.r This

paper describes the methodology to estimate PM-10 emissions due to wind blown dust at

Owens Lake and the results for a 3O-month period.

BACKGROTJI\D

The dried bed of Owens Lake in Inyo County, Califomia is the largest single source of

particulate matter pollution in the United States.' The lakebed covers an area

approximately 110 square miles (285 sq. km) and is a natural saline lake at the terminus

of the Owens River. When the Owens River was diverted by 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 historic shoreline - 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 and welfare of people

livine *itni" 50 miles (80 km) of the lake.3

In 1993, the US Environmental Protection 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 controls on 16.5 square 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 area into 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 erosion and agriculture found in US EPA Guidance

documents, such as 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. Those estimation methods also assume that a fixed portion of the dust is

composed of PM-10. Wind tunnel measurements at Owens Lake showed that the

threshold friction velocity and the proportion of PM-10 in the eroding soil will change

with different surface conditions, such as soil moisture content, soil texture, and soil

binding properties.5'6 Aeolian experts have developed more complex equations to

estimate PM-10 emissions. The Shao, et al. expression for the PM-IO_vertical flux (F')

of suspended particles produced 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 and g are constants and the function l(ut", has been shown by Owen to be

almost constant.s Therefore, the ratio Fo/Q may be expressed as

Equation 2.

Where:

( tru\ F = T rrl;)

l' can be considered to be atnost constant. At Owens Lake the mass per particle size, m4

may have similar values for areas with similar surface soil textures and the binding

energy, which is related to the aggregate strength, appears to change seasonally with

precipitation, temperature and surface soil chemistry. For certain areas and periods, the

ratto of Fo /QwouId, be nearly constant if the soil particles were of similar mass and size

in that area and the binding strength was similar for tbat period in the sa:ne area.

Therefore, the ratio Fo /Q.,could be used to characteize the ratio of the vertical PM-10

flux to the horizontal sand flux for a certain area and period.

Shao and Raupauch found that Q is distributed according to Equation 3, with the flux

decreasing rapidly as you move higher above the surface. Ten sites at Owens Lake were

instnrmented to collect sand flux dataatthree different heights (15,25, and 50 cm) and

the data were used to 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, and a, b, and c 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

almost all the sand flux.

Equation 4.

llm

Q=lomdz

AWMA Owens Lake Emissions paper-E'doc
Gillette found that for Owens Lake the sand flux at 15 cm (14.5 to 15.5 cm) is

proportional to the total sand flux and that a single sand flux measurement at 15 cm can

be used as 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

referred to as the K-factor (r<). The value for Fo /Qhas the dimensiorn per unit length,

whereas, Kyis a dimensionles_s value. Previous researchers have reported F, /Q values

for other wind erosion areas.t For comparison purposes, Fo /Q [m-tJ, canbe related to .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 rate at 15 cm above the surface lglcm2/h4.

Since K,ris a dimensionless value the units for Foare the same as for q.For modeling

using CALPUFF, Fo is converted to g/s for each square kilometer. The sand flux rate is

directly measured during dust storms with instnrments centered in each square kilometer

ofa 135 squarekilometermonitoringnetwork. Directfieldmeasurementof sandflux

rates in the Dust ID Program is a key difference from other PM-10 estimation methods

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 Modeled PM-10 predictions can then be compared to

monitored concentations at 6 PM-10 monitor sites to determine the K-factor that would

correctly predict the monitored concenfration for each hour.

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 each cell to shoreline receptors can then be

AWMA Owens Lake Emissions paper-E.doc
Figure 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 months to collect data on wind erosion. Figure I shows a

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 installed at 3 sites to

continuously record dust events during daylight hours and three observers mapped dust

source areas and plumes during the storms on regular workdays. In addition, the erosion

boundaries of some source areas were mapped with the aid of a Global Positioning

System (GPS) aftsr a storm. The GPS data and the visual observer's rnaps were put into

a Geographic Information System (GIS) database. Slong with the sand flux data to

corroborate the location of dust producing areas.''

AWMA Oweng Lake Emissions paper-E.doc
Figure 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.

Sensitsm and Cox Sand Catchers

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 placed with their

seluior or inlet positioned 15 cm above the surface. Sensits measure the kinetic energy or

the particle counts of sand-sized particles as they saltate across the surface. Due to

differences in the electronic resporse of individual Sensits, they must be co-located with

passive sand flux measurement devices to calibrate the electronic output and to 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 used to test the

accuracy of Sensits and CSC's before the Dust ID Program began. The battery powered

Sensits are augmented with a solar charging system. A data logger records hourly Sensit

data during inactive periods and switches to 5-minute data during active erosion periods.

CSC's are passive instruments that are used to collect sand-sized particles that 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

withstand the harsh conditions at Owens Lake. CSC's have no moving parts and can

collect sand for a month at Owens Lake without overloading the collector. A diagram of

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 removed from the

PVC casing to measure the sand catch sample. The length of the sampling tubes and

sasings are adjusted to accommodate the amount of erosion in each area and to avoid

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

AWMA Owens Lake Emissions paper-E.doc
Figure 3. Diagram of the Cox Sand Catcher

(CSC) used to measure sand flux at Owens

Lake.

PVC casing is buried in the groun4 an adjusfrnent sleeve is used to keep the inlet heigbt

at 15 cm to compensate for surface erosion and 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 others for

wind erosion studies.la Figure 4 compares the CsCcollected sand mass to the BSNE (r2

:0.97). This comparison was also used to detemrine the apparent size of the inlet

opening of the CSC to calculate the sand flux. BSNE's have a defined inlet area size that

can be used with the collected mass to calculate the sand flux fmass/area]. The CSC's

circular design, however, does not have an obvious inlet area size for a flux calculation.

By comparing the CSC sand catch to the BSNE fltur, the CSC was found to have an

apparent inlet size of L.435 cnf . During the same tests it was found that the CSC's had

better precision than the BSNE's. The precision for 3 co-located CSC's was * 3%, while

the BSNE's had a + 8% precision.

An example of the linearity between the CSC collected sand mass and the particle count

data for a Sensit is shown in Figure 5. The Sensit particle count data was normalized for

the tbree Sensits by dividing the total particle count for each Sensit to the average of 3

CSC's summed for all the nrns. This type of comparison is routinely used to check the

calibration of individual Sensits every month, however, the calibration curve 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 Clap
l. R

l'; =;.;; {

,/

.a

v

t

Figure 4. Test results for CSC's compared

to the ayerage sand catch for 3 BSI\E's.

Figure 5. Test results for Sensit

readings compared to 3 CSC's.

0 100 200 300 400 500 600

Average Cox Sand 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

E 2oo

x

6 1oo

0

-l Fost'.o;l f=0.e7 f

6

g -4

n

ryr e

d Y

rl 6r

0 100 200 300 400 500 600 700 800 900

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 takes advantage of the good

precision and accuracy of the CSC sand catch dat4 and the 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 always matches the CSC sand catch for each monthly sampling

period and it minimizes the error in the hor:rly sand flux. Samples from most of the

CSC's were collected once a month, and l0 intensive sites were collected after every dust

storm.

PM-10 & Meteorological Data

Hourly PM-10 data were collected at 6 sites around Owens Lake using TEOM PM-10

monitors (Figure 1). These monitors were co-located with filter-based PM-10 monitors

including: PM-10 Partisols and Wedding and Andersen Hi-Vol Size-Selective Inlet PM10 monitors. A previous study by Ono, et al. showed that the TEOM PM-10 monitors

were well suited to measure wind blown dust at Owens Lake and to provide hourly PM10 dab.t5 Meteorological para:neters were me:lsured at 13 sites on and around Owens

Lake. A wind profiler was operated at Dirty Socks and later moved to the Mill Site to

collect wind data aloft to supplementhe 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 performed using the

CALPUFF modeling system, whichutilized meteorologicaldata from thirteen lO-meter

towers and an Upper Air Wind Profiler to generate wind fields using the CALMET

model.l I More information 616sd6ling is provided in a companion paper by Richmond

et al.r CALPUFF was used to model the dust impacts at six PM-10 monitor sites using

Equation 6 with the hourly sand flux (q) from each s_quare kilometer to generate the

hourly PM-10 emission rates. A K-factor of 5 x 10-'was used to initially run the model

and to generate concentration values that were close to the monitored concentrations.

Hourly K-factor values were later adjusted in a post-processing step to determine the Kfactor value that would have made the modeled concentration match the monitored

concentration at each of 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. : Background PM-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-receptorelationships between the active source area and

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 edge of

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 used to 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 was greater than2 {cm2lhr in at least one cell

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 came from the target source area. The

screening criteria deleted a number of outliers and also many points that were in the

expected K-factor range, but overall resulted in 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.

l--l"nr.-\il

;i:iljeit\\:f r\ll

.rr::'::::=i :::::l

r'Wilitt I

*':l:,1

ll't 1tlj::

,*k

s,.-.., ; h

, s:;..;;.. ^...;

:: : ]:',|:|!ry! .@tr

::.i,. ,'::i:?nsFN.\,

RBST]LTS

Sand Flux and Observation Maps

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

hand dmwn every 15 minutes during the storms. The aerial photo in Figure 6 shows an

exarnple of an Owens Lake dust storrr. The dust source areas in this photo, taken around

1989, are scatlered around a 35 square mile (90 sq. km) area of the east side 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 and to veriff plume heights

and tajectories for the CALMET and CALPLJFF models. Figure 7 shows the dust phrme

observations and the total sand flux at each site 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

were taken for the entire stomr. The observer maps also provided useful information on

the location of small dust source areas that were not caugbt by 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 mass for each CSC/Sensit site as demonstrated by this wind

blown dust event on February G8,2001.

Loae Pine Somr T.ofafsr gnrul 1000flrru ?{Aff}1 1mn

ftl

A

lt

It 1 ? 3 4 6,,K$wnGtam

!=ffi€:!-'

t\

IJI t

il

\[

t

\

a \

*

't\ \

&\

L

il\

\

1

L€,9@

t'lD l&trdg

,SS Els!*ilsdnt.$€md$E4 rEsrdt&#

Tr ft*svdEtssl

€ ShtdFlts$qreiZai

a ChE'd4i!triFdnt

t vfr&msb l\- Fwc$b*erysfs@

ffiffiq \ t'\\\

-t2-

i

j_

I

O il.L'1: l: $F: j

AWMA Owens Irke Emissions paper-E'doc
35

30

25

6

ozo

;

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 Shell Cu

o Flat Roc

--l-Stnm A,

I

trtr

E

o

,'AJ E)

E' IH -{

I

_E* G ru Ei Itr

t E

I

H

Apr-00 Apr-O'l

Date

Apr42

network. Sensits recorded sand flux every 5 minutes throughout the dust storms and

provided erosion data when plumes could not be seen, such as at night and when the

plume itself obscured the observer's view of erosion on the surface.

fsmporal and Spatial K-factors

About 1,000 hours of screened data were used to generate temporal and spatial K-factors.

Figure 8 shows the hourly K-factors for the South Area of the lakebed. The results show

scatter in the hourly values, but a fairly constant average K-factor for each stomr during

certain periods of the year. The storm average for the South Are4 as well 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 salts form annually at Owens Lake with increased precipitation and

cold temperatures.

In addition to the South Area, three other areas of the lakebed were identified for the

spatial K-factor sets: the Keeler Dunes, the North Area, and the Cenhal Area. The

boundaries of the four areas, which are shown on the map in Figure 1, were delineated by

a soil survey of the surface soil textures. All four areas showed temporal K-factor trendso

as well 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 temporal and spatial K'

factors that were generated from the screened K-factor data. The 7iVoK-factor set is

used because it was found to provide the best model performance for the high PM-10

duyr.t

0++

Jan-00

AWMA Owens Lake Emissions paper-E.doc
Table 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

212001and total emissions for one year totaled 791400 tons.

a

I

I

I

I I

I -r rr I il,.1 r ltlr. . ill

Sep-O0 Nov-O0 Jan-O1

Date

Mar41

PM-10 Emissions

Hourly, daily, and annual PM-10 emissions can be calculated using Equation 6. Figure 9

shows the daily PM-10 emissions based on the hourly sand flux for each cell and the

appropriate temporal and spatial K-factor from Table 1. The highest daily emission total

during the program was calculatedatT,2l0 tons of PM-10 (6,540 metric tons) on lv,{ay 2,

2001. The annual PM-10 emissions for Owens Lake were determined 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 influenced by the dust contols that were implemented at the

end of 2001 . Since the sampling period only covers 30 months and the lakebed emissions

are highly variable, it is likely that peak annual and daily emissions have been higher

under different conditions. For exarnple the 1995 emission 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-00
of 8,346 tons (7,587 metic tons) on April 9, 1995, and annual emissions of 279,900 tons

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

generated emission factors.3 Currently, PM-10 emissions are on the decline as the result

of dust mitigation efforts that started in 2001. Emissions are expected to continue their

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 hours that 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 temporal and 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

directions and distances from the target source area. Figure 8 shows that the hourly Kfactors generated for the South Area were very similar when generated from Olancha,

Dirly Socks, or Shell Cut.

By inspecting data plots such as the one shown in Figure 8 for each are4 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 generally appears 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 some of the largest dust storms, it is important that these changes in the surface

conditions and K-factors are defined to 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 performed on surfaces that had been observed to be active erosion areas. Individual

test runs using the wind tunnel showed a large range of K-factor values for the same sites

that could be more than 100 times different. However, as shown in Table 2, the averages

of the runs showed similal averages to those measured by the Dust ID method for the

same areas and during the same period. It is important to note that individual wind tunnel

runs measure erosion on 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 one time.

This assumes that the Dust ID K-factor is based on sand flux for alarge area and tbat 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 Central Area 9.7 x 10-" 24.1 x lO''

2/4/01 - 4t18/0r South Area 6.6 x l0-" 5.9 x l0-"

ltglot - ll/30/0r Cenfral Area 16.0 x 10-' 5.7 x 10-'

4/t9/0r - rr/30101 South Area 3.1x 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 erosion measurements that is common to all surfaces, even

when they 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 erosion area as measured using the Dust ID method.

Measurements of K-factors and wind erosion are expected to continue at 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 method can be applied in other wind erosion areas and that additional

research should be done to measure K-factors and sand flux for other places. For other

areas, the extensive monitoring perforrred for the Dust ID Project could be substantially

reduced in size or concentrated in smaller areas to locate wind blown dust sources and to

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

proportional to sand flux, a network of sand flux monitors were installed at Owens Lake.

Passive and electonic sand flux monitors were placed on 135 sq. km of the lake bed at a

I km spacing to measure the hourly sand flux at each site for 30 months. This sand flux

information was used with the CALPUFF modeling system to model PM-10 impacts at

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. Average K7 values were found to change spatially and temporally at Owens Lake.

These changes corresponded to different lake bed soil textures and to seasonal changes in

the surface erodibility. Wind tunnel tests at Owens Lake independently confirmed these

seasonal and spatial changes in K7.

The results of 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 provided valuable information 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 Lake were estimated tobe7,2l0 tons on May 2,

2001, and the annual emissions 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 helped build 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

tunnel testrng performed by the University of Guelph, which was led by Bill Nickling and

Laura Brown.

REFERENCES

Richmon4 K Ono, D.; Hardebeclq E.; Weaver, S.; Cox, B.; Barbieri, N.;

Stanley, W.; Gillette,D., Modeling Wind Blown Dust Emissions and

Demonstrating Attainment with National Ambient Air Quality Standards at

Owens Lalre, Califurnia,proposed forpresentation at2003 Annual AWMA

Conference in San Diego, CA, June 2003.

Air Data, US Environmental Protection Agency, www.epa.gov/airldata

(accessed January 2003).

Owens Valley PMn Planning Area Demonstration of Attainment State

Implementation Plan, Great Basin Unified Air Pollution Confrol Disticf

Bishop, CA" November 1998.

Approval and Promulgation of Implementation Plans; Califomia - Owens

Valley Nonattainment Area; PM- 1 0, Federal Register, 1999, 64:17 1, 48305-

48307.

t.

2.

3.

4.

AWMA Owens Lake Emissions paper-E.doo
5. Nickling, W.G.; Luttuer, C.; Crawley, D.M.; Gillies, J.A.; Lancaster, N.;

Comparison of On- and OffLake PMnDust Emissions at Owens Lake, CA

University of Guelph, Guelpb, Ontario, Canad4 February 2001.

Nickling, W.G.; Brown, L.J, PMrc Dust Emissions at Owens Lake California

200l,Unlersity of Guelp\ Guelpb, Ontario, Canada, h:Jy 2002.

Shao,Y.; Raupach, M.R; Findlater, P.A.; J. Geophys. Res., 1993,98D,

r27t9-12726.

Owen, P.R., "f. Fluid Mech.,1964,20,225-242.

Shao, Y.; Raupach, M.R., J. Geophys. Res.,1992,97,20559-20564.

Gillette, D.; Ono, D.; Richmon4 K., Modeling Wind-Blown Dust Fmissions

at Owens (dry) Lake, CA. Proposed for publication J. Geophys. Res.

Scire, J.S.; Stimaitis, D.G.; Yamartino,R.J.,A Users Guidefor the

CALPUFF Dispersion Model (Version 5),Earth Tech, Inc., Concord MA,

January 2000.

Owens Lalre Dust Source ldentification Program Protocol, Great Basin

Unified Air Pollution Control District, Bishop, CA, February 2000.

Sensit Data Processing and Calibrafibn, Sensit Company, Portland, ND,

April2001.

Fryrear, D.W., J. Soil Water Consertt.,1986,4L,ll7-120.

Ono, D.M.; Hardebeclg E.; Parker, J.; Cox,8.G., "f. Air & Waste Manage.

Assoc., 20000 50, 1144-t156.

KEYWORDS

Wind Blown Dust, PM-10 Emissions, Owens Lake, CALPUFF, K-factor

6.

7.

8.

9.

10.

I l.

t2.

13.

t4.

15.

-t8- AWMA Owens lake Emissions paper-E.doc