E-mail: jstout@lbk.ars.usda.gov

Journal of Arid Environments (2001) 47: 425–441

doi:10.1006/jare.2000.0732, available online at http://www.idealibrary.com on

Dust and environment in the Southern High Plains

of North America

John E. Stout

Wind Erosion and Water Conservation Research Unit USDA-ARS, Lubbock,

TX 79415, U.S.A.

(Received 3 August 1999, accepted 20 September 2000)

Continuous measurements of dust reveal the intermittent nature of dust events

within the Southern High Plains of North America. Dust events appear as

sudden peaks that project outward from a much lower background dust

concentration. The measured dust record appears to follow a regular annual

cycle with most dust events occurring in spring and considerably fewer during

other seasons. The annual dust cycle reflects seasonal changes in environmental factors such as wind speed, surface cover, and moisture conditions.

Most dust events are associated with a combination of strong winds, negligible

surface cover, and dry conditions, all of which occur most frequently during the

spring season. Wind speed alone is found to be an imperfect indicator of dust

levels in the Southern High Plains because of the moderating effects of

other important environmental factors such as humidity and surface cover.

However, if one limits consideration to dry and bare conditions, dust concentration exhibits a positive correlation with daily wind speeds above 4 m s!1 and

a negligible correlation for light winds.

( 2001 Academic Press

Keywords: dust; particulate matter; PM10; wind erosion; environment

Introduction

The following report is based on an 18-month investigation of ambient dust in the

Southern High Plains of North America conducted by the USDA-Agricultural Research

Service in 1996 and 1997 (Stout et al., 1999). During this study, an attempt was made to

obtain a continuous record of daily dust samples and a record of basic meteorological

data using a combined dust sampling system and meteorological tower located in north

Lubbock, Texas. In addition, information regarding land-surface conditions was

gathered weekly from the Texas Agricultural Extension Service. This paper summarizes

the measured data and examines the influence of environmental factors, such as surface

cover and climate, on measured ambient dust levels.

Physical setting

The Southern High Plains (SHP), shown in Fig. 1, is an immense plateau located in

north-western Texas and eastern New Mexico. The uppermost soils of the SHP were

formed by the slow and gradual process of aeolian deposition on grassland vegetation

0140}1963/01/040425#17 $35.00/0 ( 2001 Academic Press
Figure 1. The Southern High Plains of North America.

(Gustavson & Holliday, 1999). Within the last century, however, most of the natural

grassland vegetation has been converted to a vast patchwork of highly erodible cropland.

In the modern SHP, one is more likely to see intermittent periods of intense wind erosion

rather than the gradual aeolian deposition of the past. When conditions are right, wind

eroding fields emit individual dust plumes that combine with plumes from other fields to

form regional-scale dust plumes that spread across the vast expanse of the SHP and

beyond. In a sense, the entire plateau becomes a large area source that produces dust

plumes on a scale comparable to that of its source region.

Past work

Past research in arid and semi-arid agricultural environments has shown that elevated

particulate matter levels are often associated with regional-scale wind erosion events

(Chepil, 1957; Laprade, 1957; Nickling & Gillies, 1993; Marticorena & Bergametti, 1995;

Saxton, 1995). Strong winds blowing across a patchwork of cultivated fields may produce

elevated dust levels associated with regional dust storms (Sazhin, 1988; Lee et al., 1994).

Atmospheric conditions and land-surface conditions constitute the environmental

conditions which control the frequency and intensity of dust storms (Middleton, 1984;

Jauregui, 1989). Specific environmental conditions may depend somewhat on location

but clearly there are common factors. For example, negligible surface cover, low surface

soil moisture, strong winds, and low humidity are important environmental factors that

426 J. E. STOUT
are often associated with dust events (Warn & Cox, 1951; Jackson et al., 1973; Jauregui,

1989). In most cases, the exact influence of each factor on regional dust levels is still

imperfectly understood and less is understood about the combined effects of

multiple environmental factors.

Dust emissions from semi-arid regions vary in time as environmental conditions

change with the seasons (Warn & Cox, 1951; Brown et al., 1968; Smith et al., 1970;

Jackson et al., 1973; Orgill & Sehmel, 1976; Goudie, 1983; Brazel & Nickling, 1986;

Wigner & Peterson, 1987; Lee et al., 1994). Past attempts to define seasonal and

long-term variations in dust storm frequency have relied heavily on visibility observations as a surrogate for direct dust concentration measurements, (Pecille, 1973; Orgill

& Sehmel, 1976; Pollard, 1977; Changery, 1983; Goudie, 1983; Wigner & Peterson,

1987; Lee et al., 1994; Lee & Tchakerian, 1995). Before 1993, visibility was routinely

estimated each hour by a National Weather Service observer who attempted to see fixed

objects at known distances from the station. When visibility decreased to less than

7 miles, a note was made as to the reason for the reduced visibility. In semi-arid

agricultural regions, the occurrence of blowing dust was sometimes reported as the

reason for reduced visibility and, as a result, National Weather Service surface observations provide a valuable record of blowing dust that often extends as far back as 1947.

Unfortunately, visibility is a subjective measurement that depends upon the judgement of the observer and the observer’s ability to detect distant objects (Orgill & Sehmel,

1976). Thus, two observers may describe the same conditions differently and it follows

that observations taken at two different locations by two different observers

may not be directly comparable. Certainly direct measurements of dust concentration

would be preferable.

Direct measurements of particulate concentration have been obtained at various times

in many major cities in the United States by state and federal regulatory agencies

concerned with air pollution. The purpose of these monitoring programs has been to

establish whether a city or region was in compliance with current air quality standards.

As air quality standards for particulate matter have changed through the years, so has the

focus of these programs.

In the Lubbock area, samples of total suspended particulates (TSP) were obtained

from 1961 to 1982 at sampling frequencies that varied from once every 2 weeks to once

a week (Cowgill, 1970). Sampling was suspended from 1983 to 1986 until a program

focused on particulate matter with a mass median aerodynamic diameter less than 10 lm

(PM10) was initiated in 1987. In the case of PM10, sampling frequencies varied from once

every 2 days to once a week.

Data collected during these regulatory monitoring programs remains an untapped

reservoir of potentially valuable information regarding wind erosion activity within semiarid agricultural regions such as the SHP. The present study adds to and improves upon

the existing dust record in a few significant ways. First, this study was a scientific study

focused on wind erosion rather than a regulatory monitoring program focused on

compliance with air pollution standards. Second, unlike past monitoring programs

where many days were skipped between samples, here an attempt was made to obtain

a more continuous record of daily dust samples to reduce the possibility of missing major

dust events. Third, an attempt was made to look in more detail at the influence of

environmental factors such as regional surface cover and climatic factors on measured

ambient dust levels.

Experiment

From 23 March 1996 to 30 September 1997, an attempt was made to obtain a continuous record of daily dust samples (PM10) at a site located within the cotton-growing

region of the SHP of North America. To obtain a regionally representative measure of

PM10, careful attention was paid to the placement of the sampling system. Locations

DUST AND ENVIRONMENT IN THE HIGH PLAINS OF NORTH AMERICA 427
*Names are necessary to report factually on available data; however, the USDA neither guarantees nor

warrants the standard of the product, and the use of the name by USDA implies no approval of the product to

the exclusion of others that may also be suitable.

Figure 2. Schematic drawing of the continuous dust sampling system.

where a single eroding field might dominate the measurements were avoided since such

local dust conditions may not represent true regional conditions. Congested sites were

avoided so that industrial sources or heavy automotive traffic would not contribute

significantly to the measurements. A grassland site was chosen since the site itself would

not be a significant dust source and the site would not change significantly through

time as surface conditions in the surrounding agricultural region varied seasonally. Such a

site can provide PM10 measurements that reflect true regional dust conditions as they

change with time.

A satisfactory site was found in north Lubbock, Texas at Lubbock Lake Landmark

State Historical Park. Contrary to its name, the landscape of Lubbock Lake is predominantly grassland with a total area of 1)5 km2

. This permanent grass cover contrasts

sharply with the surrounding agricultural land which can quickly change from fully

vegetated to completely bare in a matter of days. The non-eroding grassland provides

a buffer zone between the surrounding agricultural fields and the sampling location.

For winds blowing out of the east, south and west the nearest agricultural field is more

than 1 km away. The grass buffer zone to the north is only 200 m due to a hay field

that is located just north of the park boundary.

Continuous dust sampling system

A 2-m-tall tower was outfitted with four dust samplers as well as meteorological

instrumentation, as shown schematically in Fig. 2. The key components of the tower

include a data logger, a data storage module, a 12-V battery charged by a solar panel, four

PM10 samplers, and an array of meteorological instruments that will be described later.

PM10 was measured with Airmetrics Minivol samplers*. While the Minivol is not

a federal reference method sampler, a recent intercomparison study revealed that it

428 J. E. STOUT
provides results that closely approximate the federal reference method (Dutcher et al.,

1999).

Each sampler was connected via vinyl tubing to a separate filter holder assembly

which consisted of a rain-protected inlet, a greased impaction plate and a 47-mmdiameter filter holder. Dried and pre-weighed polycarbonate filters (0)6-lm pore size)

were installed in each of the four filter holders.

Continuous sampling was accomplished by running a single sampler each day from

midnight to midnight local time. The active sampling pump pulled 5 l min!1 of air

across its assigned filter. After 24 h, the active sampler was switched off, and the

next sampler in line was activated so that it pulled air through the next filter assembly for

24 h. This process was repeated as the system cycled through all four samplers in

a continuous loop. Every few days, exposed filters were replaced by fresh filters.

Exposed filters were then dried and weighed to determine the daily mass collected. PM10

concentration was calculated by dividing the sample mass by the total volume of air that

passed through the filter in 24 h (7)2 m3

) and the result was reported in units of lg m!3

of air.

Meteorological variables measured at the same site include wind speed, wind direction, air temperature, and relative humidity. All variables were measured at a common

height of 2 m. Variables were sampled each second and averaged over one hour before

output to final storage. Later, hourly values were averaged over 24 h to match the daily

dust sampling period.

Results and discussion

The sampling tower was completed and dust sampling began on 23 March 1996. Data

collected from 23 March 1996 to 30 September 1997 appear in Table 1.

The annual dust cycle in the Southern High Plains

Daily PM10 values measured at the Lubbock Lake site are plotted as a time-series in

Fig. 3. Although the measured dust record is not long enough to conclusively define

normal annual dust trends, the dust record does appear to follow a pattern established by

past visibility studies (Orgill & Sehmel, 1976; Pollard, 1977; Goudie, 1983; Peterson

& Gregory, 1993). Within the SHP, most observers agree that the majority of

dust events occur during the spring season due to a combination of strong winds

and a preponderance of dry and bare soils (Sidwell, 1938; LaPrade, 1954; Lee et al.,

1994).

The arithmetic mean concentration from 23 March 1996 to 23 March 1997 was

19)8 lg m!3, a value well below the 50 lg m!3 annual air quality standard established

by the United States Environmental Protection Agency (Federal Register, 1987). The

relatively low annual PM10 concentration is a result of long periods of relatively dust-free

conditions punctuated by intermittent dust events.

Dust episodes appear as peaks that dramatically thrust outward from a much lower

background concentration. The two highest PM10 values were recorded during Spring

1996 with values of 166)0 lg m!3 on 13 April 1996 and 116)8 lg m!3 on 24 March

1996. The third highest value was recorded during Spring 1997 with a value of

92)8 lm!3 on 27 March 1997.

The frequency of occurrence of dust events with a daily PM10 concentration twice the

annual concentration (in this case, greater than 40 lm!3 ) was calculated and the results

are shown in Table 2. Note that the frequency for any season is generally very low

indicating the intermittent nature of dust events. Frequency values for the spring season

were clearly much higher than any other seasonal period with frequencies of 11 and eight

DUST AND ENVIRONMENT IN THE HIGH PLAINS OF NORTH AMERICA 429
Table 1. Daily PM10 measurements obtained at the Lubbock Lake site from 23 March 1996 to 30 

PM10 measurements (lg m!3) at Lubbock Lake

1996 1

Day Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. 

1 15)4 22)9 15)7 19)3 32)5 19)2 15)0 11)0 7)1 21)3 38)3 9)7 17)5 

2 40)4 23)9 11)9 22)4 32)5 16)9 21)7 9)0 10)1 16)5 22)1 8)3 13)9 

3 28)6 29)4 — 14)7 29)9 17)5 16)8 24)2 13)3 81)8 32)1 9)3 3)1 

4 12)5 27)1 — 22)2 19)6 8)9 16)9 15)7 11)4 19)6 16)7 10)4 14)6 

5 — 32)6 40)7 28)2 21)5 12)2 18)6 18)3 14)6 19)0 9)9 12)5 36)5 

6 11)8 30)7 38)1 23)3 35)1 17)9 14)0 13)2 19)7 7)8 6)4 10)1 11)8 

7 8)8 32)5 21)7 18)1 21)0 16)4 16)9 19)7— 4)4 9)4 15)0 18)2 

8 — 23)6 16)7 21)3 7)9 6)9 14)0 20)4 21)5 6)1 14)7 11)9 — 

9 13)5 33)9 17)8 16)4 10)6 13)1 18)5 26)8 23)5 4)7 13)5 13)9 1)7 

10 19)9 21)9 20)4 13)2 10)8 9)6 15)6 27)1 23)3 6)7 17)4 13)9 11)4 

11 13)9 12)9 28)6 18)1 16)3 17)8 20)0 26)0 64)0 — 18)3 16)5 7)1 

12 26)0 19)4 14)2 18)0 12)5 14)0 16)9 23)2 20)1 — 18)8 12)6 8)2 

13 166)0 20)7 17)6 21)3 18)2 14)2 16)7 16)7 18)6 — 10)4 16)5 15)7 

14 60)3 35)0 16)1 14)0 19)4 — 15)1 13)9 19)0 7)4 9)9 9)7 11)4 

15 19)0 12)8 15)3 16)0 21)8 8)5 17)6 20)0 15)4 7)5 12)5 12)2 22)8 

16 27)4 31)4 16)4 22)8 19)7 8)9 14)9 18)1 16)5 9)4 13)1 12)8 22)4 

17 30)4 35)1 18)9 24)4 26)3 2)9 10)6 7)8 19)2 11)0 17)1 18)5 21)0 

18 26)0 32)1 21)1 19)3 27)1 13)5 17)9 14)9 20)8 13)5 20)8 52)1 14)3 

19 56)5 24)9 22)8 27)4 22)5 10)4 19)6 — 23)9 11)9 16)7 21)1 14)6 

20 25)3 67)5 19)2 18)9 19)6 11)4 19)9 24)9 23)2 13)1 10)3 26)8 19)3 

21 25)6 26)3 18)1 18)1 13)8 12)5 9)7 13)3 17)9 12)4 8)5 23)3 24)0 

22 18)8 11)8 19)0 58)6 22)5 15)8 7)2 10)4 14)7 16)5 11)0 13)2 4)9 

23 82)5 29)7 22)2 22)2 22)2 10)1 18)9 10)1 31)9 14)2 39)0 8)8 18)9 12)9 

24 116)8 35)4 44)7 21)3 12)5 7)5 18)2 19)9 26)9 14)4 13)3 11)4 64)0 9)0 

25 16.9 33)3 15)7 16)1 11)5 6)7 17)1 22)8 18)9 13)1 19)2 5)3 26)4 3)3 

26 14)4 37)8 — 12)8 17)5 9)6 10)8 49)9 11)7 21)7 12)5 7)4 19)2 4)9 

27 — 22)4 — 13)3 11)8 11)9 6)0 14)4 17)5 18)6 24)9 8)6 92)8 9)3 

28 21)3 79)3 15)1 17)1 23)8 7)6 9)7 10)1 10)7 18)2 14)9 18)1 45)1 13)6 

29 61)3 15)7 16)8 26)4 23)8 5)3 10)4 11)3 7)2 14)0 12)6 55)6 24)3 

30 — 24)4 17)5 16)4 18)8 14)0 10)7 16)4 4)7 21)0 14)7 17)9 12)6 

31 10)6 — 21)7 22)1 11)7 25)8 32)1 16)5 

Each value represents a daily PM10 concentration (lg m!3) arranged by month and day. Missing values, marked by a dash, are m

battery or sampling pump malfunctions that caused the system to shut down before a full 24-h sample was completed.
Figure 3. Daily PM10 measured at the Lubbock Lake sampling site in north Lubbock, Texas

from 23 March 1996 to 30 September 1997.

dust events per season during spring 1996 and 1997, respectively. Values were around

one or two dust events per season for any other season.

The seasonal mean PM10 concentration, cN, was calculated and the results are compiled

in Table 2. The largest seasonal mean values were recorded during Spring 1996 and

Spring 1997. The seasonal mean concentration was lowest during Winter 1996}1997.

The standard deviation of the daily PM10 concentration pc provides a measure of the

day-to-day variability of dust levels and the ratio pc/c6, called the coefficient of

variation, provides a relative measure of the dispersion about the mean. Values of pc and

pc/c6calculated for each season are included in Table 2. Both pc and pc/c6were generally

highest during spring and winter and lowest during summer and fall. High values of

pc and pc/c6during spring and winter reveal the highly variable nature of spring and winter

dust storm seasons where intermittent dust events are more frequent.

In summary, the measured dust record appears to follow an annual cycle with the

following characteristics:

(1) Dust events occur most frequently during spring and least frequently during

summer;

(2) The seasonal mean concentration is highest during spring;

(3) Maximum PM10 values are considerably higher during spring than any other

season; and

(4) Day-to-day variability is highest during spring and winter.

Environmental factors

The annual dust cycle reflects seasonal variations in environmental conditions (Smith

et al., 1970; Brazel & Nickling, 1987; Wigner & Peterson, 1987; Jauregui, 1989). The

regional dust system may be conceptualized as a simple dynamic system where strong

winds act upon the regional surface which then emits dust thereby increasing the

regional dust concentration. For a given wind condition, regional dust emissions are

modulated by environmental factors such as soil moisture and surface cover. Regional

soil moisture conditions are not routinely reported by the National Weather Service nor

DUST AND ENVIRONMENT IN THE HIGH PLAINS OF NORTH AMERICA 431
Table 2. 1996}1997 seasonal statistics for PM10 measurements at Lubbock Lake

Season

Number of

observations

Maximum

daily PM10

concentration

lg m

!3

Dust event

frequency

(PM10'40 lg m!3)

events/seasons

Seasonal mean

PM10

c

6

lg m

!3

Seas

Spring 1996 80 166)0 11 29)3 Summer 1996 93 58)6 1 17)7 Fall 1996 88 64)0 2 17)4 Winter 1996}1997 86 81)8 2 15)8 Spring 1997 91 92)8 8 19)6 Summer 1997 87 41)7 1 18)3 
Figure 4. Daily 2-m wind speed measured at the Lubbock Lake sampling site. Note that strong

winds occur most frequently during spring and less frequently during summer.

by any other agency. However, there are other meteorological variables, such as relative

humidity and precipitation, that may be used as indicators of soil moisture if interpreted

properly.

Wind and dust

Perhaps the most important environmental factor that influences dust concentration is

wind. Although other factors may moderate dust emissions, wind provides the energy

that drives the wind erosion process. Here we focus on daily-mean wind speed since dust

samples were obtained over a 24-h period. As shown by Durst (1960) and Stout (1998),

long-period mean winds are generally much lower than the intermittent short-period

gusts which actually produce the blowing dust.

Daily wind speed, measured at a height of 2 m, is plotted as a time series in Fig. 4.

Note that wind speed exhibits seasonal variations similar to the observed dust cycle with

strongest winds occurring during spring and weakest winds during summer.

As summarized in Table 3, the seasonal-average wind speed was considerably higher

during spring than any other season and spring had the highest fraction of days with

daily wind speed, u, above 5 m s!1 whereas summer typically had the lowest seasonalaverage wind speed and the lowest frequency of windy days.

Daily PM10 concentration is plotted as a direct function of the daily 2-m wind speed in

Fig. 5. Note that all PM10 values greater than 80 lg m!3 occurred when daily wind

speed was greater than 6 m s!1. When wind speed fell below 4 or 5 m s!1 PM10 values

generally clustered near the annual average of around 20 lg m!3

. There is considerably

more variability of PM10 values for higher wind speeds. For example, PM10 values as low

as 11 lg m!3 and as high as 166 lg m!3 were both associated with daily wind speeds

between 6 and 7 m s!1

. Such a wide range of possible PM10 values for the same wind

conditions reveals the influence of environmental factors other than wind, such as

surface soil moisture and surface cover.

The large amount of scatter evident in Fig. 5 is also partly due to the 24-h averaging

period. Most filter-based dust samplers, including the samplers used during this experiment, are designed to obtain samples over a 24-h period due to the fact that air quality

regulations are based upon a daily-average concentration. However, dust events sometimes last only a few hours. A short but intense dust event may not be strongly correlated

DUST AND ENVIRONMENT IN THE HIGH PLAINS OF NORTH AMERICA 433
Table 3. Seasonal wind statistics for Lubbock Lake

Season Seasonal average u6 m s!1

Seasonal frequency of

windy days (u'5ms!1

)

number/season

Spring 1996 4)44 32

Summer 1996 2)76 0

Fall 1996 3)16 9

Winter 1996}1997 3)22 8

Spring 1997 3)90 15

Summer 1997 3)02 1

Figure 5. PM10 plotted as a function of the daily 2-m wind speed.

with a 24-h average wind speed. One would expect a clearer picture of the relationship

between wind speed and dust concentration if hourly or shorter period sampling were

used.

Surface soil moisture and dust

With regard to wind erosion, it is the soil moisture content of the few millimeters at the

soil-air interface that is critical. If this thin surface layer is sufficiently moist then

individual grains tend to adhere to each other and thereby increase the resistance of the

surface to wind erosion (Saleh & Fryrear, 1995; McKenna-Neuman & Scott, 1998).

During periods of high insolation and low relative humidity, this thin surface layer can

dry quickly even when there is plentiful subsurface soil moisture.

Monitoring the moisture content of this thin surface layer across a region as large as

the Southern High Plains is not easily accomplished nor is this type of information

434 J. E. STOUT
Figure 6. PM10 plotted as a function of the daily minimum relative humidity. Note that most dust

events occur when the daily minimum relative humidity falls below 30%.

readily available. There is, however, at least one practical alternative to the use of surface

soil moisture.

The moisture content of the upper soil surface is intimately linked to the moisture

content of the atmosphere directly above it (J.M. Gregory, pers. comm.) Dry winds

extract moisture from the surface more quickly than subsurface moisture can replenish

it, leaving the uppermost soil surface desiccated and susceptible to wind erosion. On the

other hand, moist air can produce a moistening of the surface layer as well as a reduction

in evaporation. Thus, it is reasonable to assume that relative humidity may be used as an

indicator of surface soil moisture at the soil-air interface.

During a typical day, relative humidity follows a daily cycle driven by diurnal

temperature variations. Relatively moist conditions occur in the early morning whereas

the minimum relative humidity often occurs around mid-afternoon. It is during this dry

period, when relative humidity is at a minimum , that the potential for blowing dust is

maximum.

A plot of measured daily PM10 concentration as a function of the daily minimum

relative humidity is shown in Fig. 6. Note that most dust events were associated with

a daily minimum relative humidity below 30%. In fact, all PM10 values above 80 lg m!3

occurred when the minimum relative humidity fell below 30% and of the 25 values above

40 lg m!3, 23 occurred when the minimum relative humidity fell below 30%.

It is also clear from Fig. 6 that it is possible to have low relative humidity without

a large PM10 concentration. In other words, low relative humidity appears to be

a necessary condition for a high PM10 concentration, but not a sufficient condition.

There are other important factors, including wind speed, that influence PM10 but are

independent of relative humidity. Thus, unlike wind speed, relative humidity should be

viewed as a factor that can influence or temper dust emissions rather than as a driving

force.

Rainfall is another commonly measured meteorological variable that can provide an

indication of moist regional surface conditions if interpreted properly. However, as has

been pointed out by Goudie (1983), the relationship between dust and rainfall is not

a simple one. For example, rainfall at a sampling site may not be a good indication of

rainfall at dust source areas located upwind. In addition, a wind event may precede a rain

DUST AND ENVIRONMENT IN THE HIGH PLAINS OF NORTH AMERICA 435
Figure 7. PM10 plotted as a function of daily precipitation. Note that there are no daily PM10

values greater than 40 lg m!3 for any day with measurable rainfall.

event so that daily rainfall may have little direct correlation with daily dust concentration.

On the other hand, rainfall is generally associated with moist atmospheric conditions,

and as shown in the previous section, humid conditions can influence ambient dust

levels by influencing the moisture content of the uppermost soil surface. Thus, although

one may not expect a strong direct correlation between rain and dust, the two may be

indirectly correlated through humidity and the effects of humidity on soil moisture.

Since rainfall is much easier to measure than humidity and since rainfall data are often

more readily available than humidity data, it may be of some interest to at least explore

the relationship between rainfall and dust concentration.

Daily rainfall for Lubbock, Texas was obtained from the National Climatic Data

Center through a publication called Local Climatological Data (National Climatic Data

Center, 1996}1997). The National Weather Service records rainfall at Lubbock International Airport which is located approximately 10 km north-east of the Lubbock Lake

dust sampling site. Due to the design of the tipping-bucket rain gage used by the

National Weather Service, the smallest amount of rainfall that could be resolved was

0)25 mm (0)01 inches). Trace amounts of rainfall that amounted to less than 0)25 mm

were occasionally noted in the precipitation record but in this analysis they were treated

as days with no rainfall. PM10 concentration is plotted as a function of daily rainfall in

Fig. 7. Note that there are no daily PM10 values greater than 40 lg m!3 for any day with

measurable rainfall.

Surface cover and dust

The region surrounding Lubbock, Texas is significantly impacted by agriculture.

According to the 1997 Census of Agriculture, cropland accounts for 76% of the total

acreage within Lubbock County and cotton acreage makes up nearly 84% of harvested

cropland. Each year, cotton fields in the SHP pass through a regular cycle. Planting

begins in May and is completed by mid-June. As cotton plants emerge, they begin to

provide minimal ground cover and wind erosion protection. As plants reach the growth

stage called first square, the point at which cotton plants begin setting blooms, individual

plants have sufficient height and adequate leaf and stem area to provide significant

protection of the soil surface. In the SHP, squaring begins in late June and finishes in

436 J. E. STOUT
Table 4. Local field conditions as reported by the Texas Agricultural Extension

Service and the inferred surface cover fraction

Date

Fraction

squared

Fraction

harvested

Surface

cover

fraction Date

Fraction

squared

Fraction

harvested

Surface

cover

fraction

6/7/96 0 0 0 11/15/96 1 0)50 0)50

6/14/96 0)04 0 0)04 11/22/96 1 0)55 0)45

6/21/96 0)06 0 0)06 11/29/96 1 0)90 0)10

6/28/96 0)400)4 12/13/96 1 0)95 0)05

7/5/96 0)65 0 0)65 1/3/97 1 1 0

7/12/96 0)85 0 0)85 6/6/97 0 0 0

7/19/96 0)98 0 0)98 6/13/97 0)02 0 0)02

7/26/96 1 0 1 6/20/97 0)15 0 0)15

10/4/96 1 0 1 6/27/97 0)65 0 0)65

10/11/96 1 0)03 0)97 7/4/97 0)95 0 0)95

10/18/96 1 0)04 0)96 7/11/97 0)95 0 0)95

10/25/96 1 0)08 0)92 7/18/97 1 0 1

11/1/96 1 0)19 0)81 10/3/97 1 0 1

11/8/96 1 0)35 0)65 10/10/97 1 0)01 0)99

mid-July. Between squaring and harvest, most fields have adequate ground cover to

protect the soil surface in all but the most extreme wind conditions. Protective vegetative

cover is removed during harvest, which takes place from October to December. After

harvest, fields lie bare and exposed from January to May.

Information regarding local field conditions was obtained from the Texas Agricultural

Extension Service (TAES). A weekly ‘‘Crop Report’’ is prepared by Lubbock County

extension agent C. Mark Brown which provides a weekly measure of the fraction of

cotton fields that are planted, squaring, and harvested. Due to similarities in regional

farming practices and cropping patterns, information obtained from the Lubbock

County Crop Report can be extended to a vast region around the sampling site that

includes surrounding counties.

Regional cover fraction is defined here as the fraction of fields where plants have

reached first square or a growth stage beyond first square minus the fraction of fields that

have been harvested. Table 4 contains the fraction squared and fraction harvested as

reported by TAES and the estimated surface cover fraction. Linear interpolation was

used to estimate daily surface cover from reported weekly values.

PM10 is plotted along with surface cover in Fig. 8. The results reveal that most of the

large dust events occurred when regional surface cover was at a minimum. As the cotton

crop grows to cover and protect the surface in the late summer and early fall (July

through October), dust storms become less frequent and the background concentration

is reduced significantly.

PM10 values are plotted as a direct function of surface cover in Fig. 9. Note that of the

25 PM10 values above 40 lg m!3

, 21 occurred when the cover fraction was zero. The

results also indicate that values above 40 lg m!3 can occur when the surface cover

fraction is near unity, especially when winds are extreme and cotton plants are not fully

mature.

Conditional dust plots

A somewhat clearer picture of the relationship between wind and dust is obtained when

these data are viewed under more restricted conditions. For example, a semi-log plot of

DUST AND ENVIRONMENT IN THE HIGH PLAINS OF NORTH AMERICA 437
Figure 8. PM10 time-series plotted along with surface cover fraction. Note that most dust events

are associated with negligible surface cover.

Figure 9. PM10 plotted as a function of surface cover fraction.

PM10 as a function of wind speed for the conditions of bare cotton fields (surface

cover"0) and dry conditions (RHmin(30%) is shown in Fig. 10. Although there is still

considerable scatter, one can clearly see two distinct regimes.

For daily wind speeds below about 3 m s!1, the correlation coefficient between

wind speed and PM10 concentration is 0)02 indicating that there is little correlation

between wind speed and PM10 concentration for light winds since few wind gusts

exceed the wind erosion threshold and, as a result, few if any fields emit dust. For

such light wind conditions, the origin of airborne particulate matter is not related to the

wind erosion process and this is reflected in the low correlation between wind speed and

PM10.

On the other hand, for daily wind speeds greater than 4 m s!1 the conditional

correlation between wind speed and PM10 is much stronger with a correlation coefficient of 0·6. This indicates that for dry and bare conditions, the regional surface is

responding to strong wind forces by emitting dust into the atmosphere. In other words,

as wind speed increases, more cultivated fields begin to emit dust and those fields already

438 J. E. STOUT
Figure 10. PM10 plotted as a function of the daily 2-m wind speed for the conditions of no

surface cover and daily minimum relative humidity less than 30%.

emitting dust tend to emit increasing amounts of dust. As a result, dust concentration

tends to increase rapidly with wind speed under these conditions.

Conclusions

The arithmetic mean dust concentration for the SHP is low (&20 lg m!3) indicating

a normally clear or dust-free atmospheric condition. This normally low dust condition is

intermittently interrupted by dust events that occur most frequently during the spring

season. Thus, the Southern High Plains dust record appears to follow a regular annual

dust cycle characterized by frequent dust events during spring and few, if any, during

the summer and early fall.

The annual dust cycle results from seasonal changes in environmental conditions.

Important environmental factors include wind speed, relative humidity, precipitation

and surface cover. Most dust events are associated with periods with low humidity

(RHmin(30%), no precipitation, negligible surface cover, and daily wind speeds greater

than 4 m s!1

.

Wind speed alone is an imperfect indicator of high dust levels in the Southern High

Plains since other environmental factors such as surface cover and moisture can

significantly reduce regional dust levels, even during high winds. During dry and bare

conditions, there are two distinct regimes that are evident. For dry and bare conditions

with light winds (u(3ms!1), wind speed remains below threshold and PM10 concentration is independent of wind speed. For dry, bare conditions, and strong winds

(u'4ms!1

), PM10 concentration exhibits a positive correlation with wind speed.

I would like to thank H. Dean Holder for helping to construct the sampling system. Bret Lamblin,

Denny Runyon, and Walter Waybright for changing the filters and servicing the samplers.

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