GRAND CANYON HAZE:
Its Magnitude, Variability, Composition, and Sources
Technical Background Document
for the
Grand Canyon Visibility Transport Commission
September 5, 1991
Prepared for
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
Under Subcontract to
E. H. Pechan & Associates, Inc.
3514 University Drive
Durham, NC 27707
Prepared by
Douglas A. Latimer
Latimer & Associates
2769 Iris Avenue, Suite 117
Boulder, Colorado 80304
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TABLE OF CONTENTS
1. INTRODUCTION 1
Suggestions for Further Reading 2
2. BACKGROUND INFORMATION 4
Light Extinction and Visual Range > 4
Effect of Particle Size on Light Scattering Efficiency 6
Effect of Relative Humidity on Light Scattering Efficiency 11
Typical Light Extinction Efficiencies 17
Chemical Pathways for Secondary Particle Formation 17
3. EXISTING VISIBILITY IN THE GRAND CANYON 21
4. COMPOSITION OF GRAND CANYON HAZE 31
5. REGIONAL EMISSIONS OF HAZE PRECURSORS 50
6. SOURCE REGION CONTRIBUTIONS TO GRAND CANYON HAZE ... 61
7. SPECIFIC EMISSION SOURCE CONTRIBUTIONS TO
GRAND CANYON HAZE 73
Back Trajectory Analysis 73
Regional Haze Deterministic Modeling 73
Simple Source-Receptor Relationships 84
Contributions of the Navajo Generating Station 88
8. VISIBILITY AND SULFATE TRENDS AT GRAND CANYON 92
9. GOALS AND STRATEGIES FOR REDUCING HAZE
IN THE GRAND CANYON 101
REFERENCES 107
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"... the Grand Canyon is not a show place, not a beauty spot, but a revelation
. . . even to remember that it is still there lifts up the heart. If I were an
American, I should make my rememberance of it the final test of men, art, and
policies. I should ask myself; Is this good enough to exist in the same country
as the Canyon? How would I feel about this man, this kind of art, these political
measures, if I were near that rim? Every member or officer of the . . .
Government ought to remind himself, with triumphant pride, that he is on the
staff of the Grand Canyon." J. B. Priestly
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1. INTRODUCTION
The 1990 amendments to the Clean Air Act added Section 169B which required the U.S.
Environmental Protection Agency (EPA), among other things, to establish the Grand
Canyon Visibility Transport Commission. This commission has the following duties:
To "assess the scientific and technical data, studies, and other currently
available information, including studies ... pertaining to adverse impacts on
visibility from potential or projected growth in emissions."
To "issue a report to the Administrator [of the EPA] recommending what
measures, if any, should be taken under the Clean Air Act to remedy such
adverse impacts."
This document is designed to provide the reader with a brief technical overview of the
nature and causes of visibility impairment in the Grand Canyon and other nearby national
parks and wilderness areas.
In this report we present the theoretical and empirical basis for understanding visibility
impairment in the Grand Canyon. This report is based on a brief review of the literature.
The objective of the report is to provide the reader with an overview of the visibility
literature, especially as it relates to visibility and aerosol research and visibility improvement
planning activities in the Grand Canyon.
In Chapter 2 we present background information for light extinction and visual range, the
effects of particle size and relative humidity on light scattering efficiency, and particle
formation. In Chapter 3 we present recent visual range data for the Grand Canyon. The
chemical composition of haze in the Grand Canyon is discussed in Chapter 4. Chapter 5
summarizes regional emissions in the western United States. In chapters 6 and 7, the
various studies that have estimated the source regions and specific emission sources that
contribute to haze in the Grand Canyon are summarized. Chapter 8 discusses the trends
in visibility and sulfate concentrations in the Grand Canyon. In Chapter 9, possible goals
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and strategies for improving visibility in the Grand Canyon are discussed briefly. References
are provided at the back of the report as well as appendixes showing Grand Canyon
visibility data and transport winds in the Southwest.
SUGGESTIONS FOR FURTHER READING
Since this document is an overview document, the reader may wish to delve further into
some of the details. In addition to the references listed at the back of this document, the
following references are particularly recommended:
Introduction to Visibility
Malm, W. C. 1983. Introduction to Visibility. National Park Service. Fort Collins, CO.
EPA. 1979. An EPA Report to Congress. EPA-450/5-79-008. Office of Air Quality
Planning and Standards. Research Triangle Park, NC.
Overview of Regional Haze
EPA. 1985. Developing Long-term Strategies for Regional Haze: Findings and
Recommendations of the Visibility Task Force. Office of Air Quality Planning and
Standards. Research Triangle Park, NC.
Trijonis, J., R. Charlson, R. Husar, W. Malm, M. Pitchford, W. White. 1989. NAPAP State
of Science and State of Technology. Visibility: Existing and Historical Conditions --
Causes and Effects. SOS/T Report No. 24. National Acid Precipitation Assessment
Program. Washington, DC.
NFS. 1988. Air Quality in the National Parks. A Summary of Findings from the National
Park Service Air Quality Research and Monitoring Program. National Park Service,
Air Quality Program. Denver, CO.
Transactions and Special Journals
APCA. 1987. Visibility Protection: Research and Policy Aspects. Transactions. Edited
by P. Bhardwaja. Air Pollution Control Association. Pittsburgh, PA.
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AWMA. 1990. Visibility and Fine Particles. Transactions. Edited by C. V. Mathai. Air
& Waste Management Association. Pittsburgh, PA.
White, W. H. 1981. Plumes and Visibility: Measurements and Model Components. Atmos.
Environ. Special Issue. 15:1785-2406.
Visibility Modeling
Latimer, D. A. et al. 1978. The Development of Mathematical Models for the Prediction
of Anthropogenic Visibility Impairment. EPA-450/3-78-110a,b,c. U.S. Environmental
Protection Agency. Office of Air Quality Planning and Standards. Research
Triangle Park, NC.
Latimer, D. A, H. Hogo., et al. 1985a. Modeling Regional Haze in the Southwest: A
Preliminary Assessment of Source Contributions. SYSAPP/85-038. Systems
Applications Inc. San Rafael, CA.
Latimer, D. A, et al. 1985b. Uncertainties Associated with Modeling Regional Haze in the
Southwest. SYSAPP-85/108. Systems Applications Inc. San Rafael, CA. prepared
for American Petroleum Institute, API Publication No. 4403. Washington, DC.
Regulatory Options
Connolly, S. J. and D. A. Latimer. 1985. Analysis of Options for Regulating Visibility in
the Western United States. SYSAPP-85/053. Systems Applications Inc. San Rafael,
CA.
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2. BACKGROUND INFORMATION
Before we discuss existing haze in the Grand Canyon, its variability over time, its chemical
composition, and regional sources in subsequent chapters, we present here some basic
technical background so that the terms and concepts presented later are well understood.
LIGHT EXTINCTION AND VISUAL RANGE
Visibility is commonly defined as the farthest distance at which a black object is visible. The
term visibility is basically interchangeable with the term visual range. Visibility is limited
by light scattered into and out of the line of sight and by light absorbed along the line of
sight. The sum of light scattering and light absorption is called light extinction. Light
extinction is usually quantified using the light extinction coefficient (bext, which can be
conceived as the atmospheric concentration of light extinction cross-sectional area. Light
extinction as units of m2/m3 or m"1. In this report, light extinction coefficients are usually
presented in the following units: inverse kilometers (km"1) and inverse megameters or
millions of meters (Mm"1).
The single most important equation in visibility science is the Koschmieder equation that
relates the light extinction coefficient and the visual range (Koschmieder, 1924; Middleton,
1952):
rv = - m(Cmin)/bext (1)
where rv is the visual range, Cmin is the minimum perceptible contrast between two objects
(e.g., a mountain and the horizon sky), and bext is the light extinction coefficient.
The value of Cmin, the threshold or just barely noticeable contrast varies depending on
observer and viewing conditions. Typically, Cmin is assumed to be 2 percent (0.02).
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Substituting this value into the Koschmieder equation yields the following:
rv = 3.91/bext . (2)
This is the most generally used expression to relate visual range or visibility and light
extinction. However, it appears that airport visibilities are better explained by using a larger
value of Cmin = 0.05 (5 percent contrast), which yields the following expression (see
Appendix A of Latimer and Ireson, 1988):
rv = 3.00/bext . (3)
The light extinction coefficient (bext) is the sum of the light scattering coefficient (bscat) and
the light absorption coefficient (babs). Light scattering results from the natural Rayleigh
scatter (b^) from air molecules (which causes the natural blue sky) and the scattering
caused by suspended particles in the atmosphere (aerosols). Particle scatter (bsp) can be
caused by natural aerosol (e.g., wind-blown dust and fog) or by man-made aerosol (e.g.,
sulfates, nitrates, carbonaceous aerosol and other fine and coarse particles). Light
absorption results from gases (bag) and particles (bap). Nitrogen dioxide (NO2) is the only
major light absorber in the atmosphere; its strong wavelength-dependent scatter causes
yellow-brown discoloration if NO2 is present in sufficient quantities in the atmosphere. Soot
(elemental carbon) is the dominant light absorbing particle in the atmosphere. Thus, the
total light extinction is the sum of its components:
bext = bsca, + tabs = (^Ray + V + (l)ag + bap) . (4)
The particle light scattering coefficient (b^), in turn, is composed of the contributions from
individual species. As we will discuss later, fine particles are much more efficient at light
scattering (per unit mass) than larger particles. Thus, it makes sense to divide the
contributions to b^ into the contributions from various species of fine and coarse particles.
In future chapters of this report, we specifically evaluate the following components of fine
particles (those with diameters less than 2.5 n m): sulfate (SO4=), nitrate (NO3~), organic
carbon, and elemental carbon (soot, also known as light absorbing carbon, LAC) as well as
other fines.
In addition to these chemical species, the effect of water that may be associated with sulfate,
nitrate, and some organics needs to be considered in the overall assessment of light
extinction. Finally, the coarse fraction of PM-10 (those with diameters between 2.5 and 10
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n m) and giant particles (those with diameters greater than 10 p m) were separately
considered.
The light extinction coefficient can be written as the sum of the products of the
concentrations of individual species and their respective light extinction efficiencies:
be* = bRay + 2 /JjC, , (5)
where ft{ is the light extinction efficiency (m2/g) of species i, Q is the atmospheric
concentration of species i (/£/m3), and the summation is over all light-interacting species
(i.e., sulfate, nitrate, organic carbon, elemental carbon, other fine particles, coarse particles,
giant particles, and NO2). The above units when multiplied yield units for bext of 10"6 m"1
or (106 m)"1, or as we prefer to label it here, inverse megameters (Mm'1).
When this last equation is substituted into the Koschmieder equation, we find that visibility
is roughly inversely proportional to particle concentration, with the particles having the
largest ft; having the largest effect. This inverse relationship is illustrated schematically in
Figure 1. This figure shows the effect of incrementally adding 1 ^ g/m3 of fine particulate
having ft = 4 m2/g. Because of the inverse (hyperbolic) nature of the Koschmieder
equation, the first few increments of fine particles has the largest effect on visibility. For
example, the first microgram of fine particles reduces visual range by 29 percent, from 391
to 279 km. However, subsequent additions cause ever smaller percentage reductions (i.e.,
22, 18, 15, 13 percent, etc.) Thus, small increments of fine particles to a very clean
background can have quite significant effects. This point is important to remember when
we discuss the "clean air corridor" concept later.
The light extinction coefficient and each of its components is a function of the wavelength
of light. Generally, light scattering and absorption is greater at the blue end of the visible
spectrum (i.e., short wavelengths) compared to the red. For visibility calculations, it is often
customary to define bext at the center of the visible spectrum. Since the visible spectrum
spans roughly from 0.4 to 0.7^m in wavelength, the middle of the spectrum (a green color)
is at 0.55 n m.
EFFECT OF PARTICLE SIZE ON LIGHT SCATTERING EFFICIENCY
The fact that fine particles have a much larger light scattering efficiency ft than larger
particles was alluded to earlier. Figure 2 illustrates the dependence of light scattering and
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Visibility - Particle Relationship
Light Extinction Efficiency = 4 m2/g
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if
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Grand Canyon
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Los Angeles
1 | j | n 5 »_ \
i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i
10 15 20 25 30 35 40
Fine Particle Concentration (ug/m3)
Figure 1. Relationship between visual range and fine particle concentrations. Each bar
shows the effect of incrementally adding 1 n g/m3 of fine particles having a light extinction
efficiency of 4 m2/g. Note that the largest percentage reductions in visual range occur with
the first particle additions. Note current conditions in Grand Canyon and Los Angeles.
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10.0
ro
u
ru
,- 1.0
0.1
Soot
Typical Nonabsorbinq
Aerosol
0.01 0.1 1.0
Particle Diameter (urn)
10.0
Figure 2. Light extinction efficiency as a function of particle size for typical aerosols and
for soot (elemental carbon or light absorbing carbon). After Bergstrom, 1973).
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absorption as a function of particle size. The figure shows the volumetric light extinction
efficiency (i.e., the li'ght extinction efficiency per unit particle volume - rather than per unit
mass). This volumetric efficiency is simply the product of ft and p, the density of the
particle. The efficiency per unit mass can be derived by dividing these values by the particle
density (p's typically vary between 1 and 3 g/cm3).
Figure 2 shows that there is a very narrow peak of light scattering efficiency between
approximately 0.1 and 2.5 n m. However, light absorption of elemental carbon (soot)
remains high for particles below 0.1 n m in diameter. This figure is appropriate for particles
of a single size; however, most atmospheric aerosols are mixtures of particles of varying
sizes. Various researchers have shown that aerosols can be characterized by combinations
of different modes of different size. In most locations, aerosols consist of three or four
modes: the nuclei, accumulation, coarse, and giant modes. The nuclei mode includes
particles smaller than 0.1 ^m in diameter, which are not effective in scattering light. The
accumulation mode, submicron mode, or fine particle mode, consisting of particles in the
range from 0.1 to 2.5 n m in diameter is the most important in determining visual range.
The coarse mode, consisting of particles with diameters between 2.5 and 10 ^m, has a much
smaller light extinction efficiency, and giant particles, those larger than 10 n m in diameter,
are even less efficient. These modes are log-normally distributed by mass (or volume).
Almost all atmospheric aerosol modes have geometric standard deviations ag of 2.
Figure 3 illustrates the light scattering efficiency of various particle modes having different
mass median diameters (Dp) and ag's. The efficiencies for the fine particle mode (typically
with a Dp of 0.3 M m) and the coarse mode (typically with Dp of 6 /* m) are highlighted. The
former is nearly an order of magnitude larger than the latter. Thus, one would need nearly
10 times more coarse aerosol than fine aerosol to cause the same light scattering effect.
Dividing the volumetric light scattering efficiencies shown in Figure 3 by a typical particle
density of 2 g/cm3, we obtain scattering efficiencies of 3 and 0.4 m2/g, respectively, for the
fine and coarse particle modes. Note from Figure 3 that fine particles can grow to larger
values of Dp (e.g., 0.5 n m) without significantly changing the light scattering efficiency
because of the relative flatness of that portion of the efficiency curve. Considering that soot
both scatters and absorbs light, it is not surprising that soot has the largest light extinction
efficiency (approximately 10 m2/g).
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10
u
a
•#«•
aa
I I I I I
0.1
0.1
f*-Submicron mode
I
Coarse mode
i i i
I I I
1.0
Mass Median Diameter
10.0
Figure 3. Light scattering efficiency for various size distributions. Values of efficiency in
figure should be divided by particle density (g/cm3) to obtain light scattering efficiency in
m2/g. Source: Adapted from Latimer et al., 1978.
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EFFECT OF RELATIVE HUMIDITY ON LIGHT SCATTERING EFFICIENCY
The issue of changes in phase between gas and aerosol is a key uncertainty in
understanding, measuring, and mathematically modeling the impacts of sulfate and nitrate
particles (Sloane and White, 1986):
Just as a cloud produces a dramatic visual effect when only a
small fraction of the water vapor changes phase, a substantial
haze results if only a fraction of the gaseous pollutant mass
enters a condensed phase. In this regard, visibility is unique
among air pollution effects; it depends not only on the amount
of air pollution but in addition on its phase. This peculiarity
greatly complicates the prediction of visibility impairment and
aerosol measurement procedures because the equilibrium
between the condensed and gaseous phases can be fragile.
Sulfates and nitrates can combine with water from the vapor phase to form solutions. Thus,
at some humidity conditions, considerable water may be associated with these species.
Although the overall light scattering efficiency is on the order of 3 m2/g for these solutions,
if the light scattering efficiency is stated in terms of the mass of dry sulfate (SO4=), the
efficiency must be larger than 3 m2/g to account for the additional mass (and volume) of
the associated water. In addition, the associated cations (H* and NH4+) must also be
included. As a result, light scattering efficiency per unit of dry sulfate can be much larger
than 3 m2/g, at higher humidities approaching or even exceeding the value for soot.
Sulfates
The effect of water and cation mass associated with sulfate can be considered by combining
coefficients with the basic light scattering efficiency as follows:
0(RH,NH4+) = k(NH/)f(RH,NH/)/J , (6)
where 0 is the effective light scattering efficiency of sulfate per dry sulfate (SO4=) mass, k
is a factor to account for associated ammonium ion (NH4*) mass, and f is a factor to
account for associated water whose total quantity is a function of relative humidity and the
form of the sulfate (whether 0,1, or 2 ammonium ions are associated with each sulfate ion).
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The coefficient k is simply the ratio of the molecular weight of the given sulfate species
[H2SO4, NH4HSO4, or (NH4)2SO4] and the sulfate ion (SO4=).
The thermodynamic equilibrium of sulfate solutions can be determined as a function of
relative humidity and the form of the sulfate, in other words, whether it is H2SO4,
NH4HSO4, or (NH4)2SO4. Less water is associated with sulfate for a given relative humidity
when less ammonia is associated with sulfate. Therefore, sulfuric acid (H2SO4) is much
more hygroscopic and efficient in scattering light than ammonium sulfate [(NH4)2SO4).
Ammonium bisulfate has a deliquescence point at approximately 40 percent relative
humidity, while ammonium sulfate has one at approximately 80 percent relative humidity.
Under most conditions at the Grand Canyon, sulfate exists in the form of ammonium
sulfate.
However, thermodynamic equilibrium may underestimate the amount of liquid water
associated with sulfate. There is evidence that aerosols, especially those that are internally
mixed (i.e., having several chemical species within a single particle or aerosol droplet),
experience a hysteresis effect. If an aerosol is cycled between high humidity (e.g., when an
air parcel is at the top of the mixed layer or at night and in the morning) and relatively low
humidity conditions, it may not achieve equilibrium, remaining supersaturated. Thus, as
shown in Figure 4, ammonium sulfate may not recrystallize at humidities below 80 percent.
Thus, sulfate light scattering efficiencies are likely to be underestimated by thermodynamic
equilibrium considerations alone.
Figure 5 shows one estimate of the effect of relative humidity on sulfate light scattering
efficiency. Note that at 94 percent relative humidity, the sulfate light scattering efficiency
is ten times what it is at 0 to 30 percent relative humidity. At 70 relative humidity, the
efficiency is twice what it is at low humidities.
Nitrates
The role played by nitrate particles in urban, regional, and layered haze and in plumes is
currently uncertain because of the volatile nature of this species. Unlike sulfate, which is
always in the paniculate phase, nitrate often remains in the gas phase as nitric acid. In
order to condense nitrate paniculate, ammonium nitrate (NH4NO3), there must be sufficient
atmospheric ammonia to react with nitric acid. Furthermore, the vapor pressure of
ammonium nitrate is strongly temperature dependent, so that even if ammonia is present
in the atmosphere, nitrate particles may not condense at moderate or high temperatures.
The volatility of ammonium nitrate particles contributes to the difficulty and uncertainties
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2.0
o
Q.
o
•t
u
I 1.5
IB
1.0
a)
30 50
70
Relative Humidity, %
8
7
6
5
4
3
2
1
0
o
Figure 4. Effect of relative humidity on particle size due to particle hygroscopicity and
deliquescence. Note that sulfuric acid particles are much more hygroscopic than ammonium
sulfate. Note the hysteresis effect for ammonium sulfate. Source: Adapted from Tang
(1980) by White (1985).
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Sulfate Relative Humidity Factor
Light Scattering Multiplier
100
0)
I
I
cc
0 10 20 30 40 50 60 70 80 90 100
Relative Humidity (RH) in Percent
Figure 5. Effect of relative humidity on light scattering efficiency of sulfate. Multiplier for
associated water per unit mass of dry sulfate.
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in most measurement programs carried out to date. These difficulties regarding phase
changes is complicated even more by the fact that ammonium nitrate is deliquescent; it
absorbs water from the atmosphere at moderate to high relative humidities. Thus, like
sulfate, the scattering efficiency of ammonium nitrate paniculate is enhanced by associated
liquid water in the paniculate droplet.
Nitrate particles will form if and only if (1) there is sufficient ambient ammonia to
neutralize gas phase nitric acid (HNO3) and (2) temperatures and relative humidities are
such that the thermodynamic equilibrium favors the formation of nitrate particles (Stelson
et al., 1979; Stelson and Seinfeld, 1982; Saxena et al., 1986; Sloane and White, 1986).
Ammonia reacts preferentially with sulfate until it is fully neutralized as ammonium sulfate
(Saxena et al., 1986). If sufficient gas phase ammonia is left after sulfate neutralization and
temperatures are low enough, ammonium nitrate particles will condense. At relative
humidity above 62%, the deliquescence point for ammonium nitrate, water vapor is taken
up in the nitrate particle (droplet), forming a water solution (Saxena et al., 1986). At these
higher humidities, a new equilibrium is established favoring more nitrate in the particulate
phase (Sloane and White, 1986).
The net result of all of the nitrate phase interactions is that ammonium nitrate particles "can
build up only in locations where sufficient ammonia is present to neutralize the sulfuric acid.
This occurs, for example, in Los Angeles and Denver, where sulfate concentrations are
relatively low compared to concentrations of ammonia" (Milford and Davidson, 1987).
White and Macias (1987) attribute the extremely low nitrate particulate concentrations
observed in the intermountain West to very low ambient nitric acid and ammonia
concentrations and to the warm temperatures during the non-winter months. Thus, we can
summarize the conditions under which fine nitrate particles are most likely to form: high
ambient concentrations of ammonia and nitric acid (e.g., Los Angeles, Denver), low ambient
concentrations of sulfate (e.g., most of the western U. S.), low temperatures (e.g., winter),
and high humidities (e.g., winter, coastal sites). Conversely, fine nitrate particles are most
unlikely to form under the following conditions: low ambient concentrations of ammonia
and nitric acid (e.g., intermountain West), high ambient concentrations of sulfate (e.g., the
eastern U.S.), high temperatures (e.g., summer), and low humidities (e.g., the Southwest).
Furthermore, if sufficient coarse particles exist that can react with nitric acid (e.g., sea salt,
alkaline soil dust), coarse nitrate particle formation is favored. As much of the subsequent
discussion bears out, these generalizations based on thennodynamic equilibrium explain
much of observed nitrate particulate behavior.
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The extreme volatility of nitrate particles makes their measurement extremely difficult and
uncertain (Sloane and White, 1986). Significant positive and negative artifacts can occur
with different measurement techniques using different filter media. For example, glass fiber
filters have a significant positive nitrate particulate artifact because they tend to adsorb
nitric acid vapor (Appel et al, 1985). On the other hand, Teflon filters may have a
significant negative artifact because nitrate particles are volatilized during or after the
sampling, resulting in a potential underestimation of m'trate particulate mass. Actual
particle nitrate concentrations tend to be about 20 to 50 percent higher than Teflon-based
measurements, on an annual average basis (Appel et al., 1985; Stevens et al., 1988; John,
1986; Mulawa and Cadle, 1985). However, others (White and Macias, 1987; Malm and
Gebhart, 1988) suggest that at least during winter conditions nitrate particle measurements
might be low by factors of 3 to 5. The denuder/nylon-filter method is the most accurate
measurement technique (Mulawa and Cadle, 1985); however, limited data are available with
this technique. Thus, in evaluating empirical studies of the importance of nitrate to total
light extinction, it is important to consider the complications caused by uncertainty in nitrate
particulate measurements.
Further complicating the definition of the role of nitrate is the fact that nitrate particles will
absorb water vapor, becoming water solutions, at high humidities (above 62%). The water
associated with the nitrate results in scattering efficiencies per unit mass of nitrate that are
much larger than dry particle efficiencies. The effect on light scattering efficiencies of liquid
water associated with aerosols has been known for a long time, but the specific effect of
associated water is difficult to quantify. Empirical studies have used a nonlinear relative
humidity term to attempt to account for this effect.
Externally mixed aerosols, those where the sulfate and nitrate exists on different particles,
exhibit the separate deliquescent points for ammonium sulfate (80% rh) and ammonium
nitrate (62% rh). Internally mixed aerosols, those where the sulfate and nitrate occur mixed
within the same particle, do not exhibit distinct deliquescent points and have less water
associated with them at a given humidity and hence have lower light extinction efficiencies.
The sulfate and nitrate particulate mixtures may also exhibit hysteresis effects in situations
where humidity is reduced, thereby causing a haze to linger.
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TYPICAL LIGHT EXTINCTION EFFICIENCIES
Typical light extinction efficiencies are approximately as follows:
Species B-t (m2/g)
Fine Particles (< 2.5 »m)
Sulfate and Nitrate (Low RH) 4
Sulfate and Nitrate (High RH) 10
Organic Carbon 3
Elemental Carbon 10
Other Fine Particles 3
Coarse Particles (2.5 n m < Dp < 10 n m) 0.4
Giant Particles (> 10 n m) 0.1
Nitrogen Dioxide 0.17
CHEMICAL PATHWAYS FOR SECONDARY PARTICLE FORMATION
In this section, we outline the main chemical reactions responsible for the production of
secondary sulfate, nitrate, and organic particles, which are significant contributors to haze
in the Grand Canyon.
Sulfate
Sulfate is formed in the atmosphere in both gas- and liquid-phase reactions as a result of
sulfur dioxide (SO2) oxidation by several species related to ozone (Seigneur, Saxena, and
Roth, 1984). In the gas phase, the most important reaction is the oxidation of SO2 by the
hydroxyl radical (OH), which is photochemically produced:
SO2 + OH -» HSO3 . . . -» H2SO4
In the aqueous phase (e.g., clouds and fogs), SO2 can be directly oxidized by dissolved
ozone, but the most important reaction is with hydrogen peroxide (H2O2). Metal-catalyzed
oxidation is generally less important:
S02 + H2O2 - H2SO4
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SO2 + O3 - ... - H2SO4
SO2 + O2 + Catalysts (e.g., Fe, Mn) -» H2SO4
Aqueous-phase SO2 oxidation in clouds and fog can be very rapid. Malm et al. (1989) found
a linear statistical relationship between SO2 oxidation rate and relative humidity, with a
maximum rate of 1.7 percent per hour at 100 percent relative humidity.
Nitrate
Nitrate particles are formed from nitric acid vapor (HNO3) when sufficient ammonia is
present and temperatures are sufficiently low. Nitric acid is formed primarily from gas-
phase reactions. During the day, the most important reaction for nitrate formation is with
the hydroxyl radical (about 7 time faster than the reaction that forms sulfate):
NO2 + OH - HNO3
At night, nitric acid is formed via a direct reaction with ozone:
NO2 + O3 -» NO3 + O2
NO3 + NO2 -» N2O5
N2O5 + H2O - 2HNO3
Aerosol Equilibrium Considerations
Sulfate and nitrate light scattering efficiency is strongly dependent on relative humidity,
temperature, and background ammonia concentrations.
Nitrate particles and associated liquid water are formed from nitric acid vapor (HNO3) at
relative humidities above 62 percent. Below 62 percent relative humidity, nitrate particles
may form if ammonia and nitrate concentrations are high enough and ambient temperatures
low enough. At typical concentrations in the West, nitrate particles are likely to form in and
near urban areas most of the year, but at high summer temperatures nitrate may volatilize.
In nonurban areas nitrate particles are likely to form only if temperatures are low and
ambient ammonia concentrations are sufficiently high.
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All sulfate (SO4=) that is formed from SO2 emissions condenses as a particle. The amount
of liquid water associated with sulfate species increases with relative humidity and can be
modeled using various empirical and theoretical relationships.
Organics and Elemental Carbon
Organic particles are emitted directly (primary species) and are also formed in the
atmosphere from gaseous precursors (secondary species). Elemental carbon is only emitted
directly. Primary organics and elemental carbon emissions originate from motor vehicles
and from natural gas, fuel oil, and wood combustion.
In the Denver area, 31 percent of primary organic particle emissions originates from motor
vehicles, 26 percent from natural gas and oil combustion sources, 27 percent from wood
stoves and fireplaces (at least in the winter), and 16 percent from other unknown sources.
Of the total primary elemental carbon emissions in Denver, gasoline vehicles contribute 7
percent; diesel vehicle, 24 percent; aircraft, 7 percent; combustion of natural gas, oil, and
coal, 20 percent, and wood stoves and fireplaces, 43 percent (Wolff et al., 1982).
The ratio of organic of elemental carbon in urban primary emissions appears to be relatively
constant, at approximately 1.5:1 (Wolff et al., 1982; Gray et al., 1984; Ferman, Wolff, and
Kelly, 1981). Ambient measurements of this ratio in various urban areas suggest that urban
organics are dominated by primary emissions and that secondary organic particle formation
is relatively insignificant (Wolff et al., 1982; Gray et al., 1984). However, we know from
laboratory (smog chamber) studies that organic particles are produced from gaseous
hydrocarbon emissions (Miller and Joseph, 1976). Secondary organic particle formation is
also suggested by ambient measurements of the ratio of the organic of elemental carbon
content of fine particle samples in suburban, rural, and remote sites (Wolff et al., 1982).
Whereas for direct emissions and urban whereas this ratio is 1.5, at other sites it is much
higher, i.e., 4.5.
Secondary organic particles are formed via two processes from two different classes of
hydrocarbons (Miller and Joseph, 1976):
Aromatics + OH -» Organics
Cyclic Olefins + O3 -+ Organics
19
-------
The first reaction is the most important pathway to production of secondary organic particles
from man-made emissions. After reacting with the hydroxyl radical, about 5 to 10 percent
of aromatics such as toluene are converted to organic particles (Miller and Joseph, 1976).
Since aromatics typically account for 15 percent of urban hydrocarbon emissions,
approximately 1 percent of total man-made reactive hydrocarbon emissions are converted
to secondary organic particles. This conversion process occurs during the day over a period
of several hours (Miller and Joseph, 1976).
The second reaction is primarily important in the production of secondary particles from
biogenic emissions. Cyclic olefins are a very minor part of anthropogenic emissions. The
primary source of these species is the biogenic emission of cyclic diolefins (terpenes).
Terpene emission factors have been estimated at between 100 and lOOO/ig/hr/m2 for typical
urban and nomirban areas (Oliver, Lundberg, and Banks, 1984). Ferman, Wolff, and Kelly
(1981) have estimated secondary particle formations rates from terpenes in the Blue Ridge
Mountains at relatively high rates, suggesting that all emitted terpenes are almost
instantaneously converted to particles.
In the West fine organic particle concentrations have been reported to range from values
as high as 7.6 n g/m3 in Denver to values of 3.4 n g/m3 in Pierre, North Dakota (Wolff et
al., 1982). At J^ake Tahoe, terpenes were typically 2 to 4 n g/m3 (Pitchford and Allison,
1984). Trijonis (1981) estimated that the average fine organic particle concentration in the
East was 4 1 g/m3 and that natural levels might be 2 1 g/m3.
20
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3. EXISTING VISIBILITY IN THE GRAND CANYON
The Grand Canyon is located in one of the cleanest and clearest areas of the Lower 48
states. Figure 6 shows the median visual range estimated for locations across the United
States based on airport visibility data (Trijonis and Shapland, 1979; Latimer and Ireson,
1980). Note that visual range in the Grand Canyon area was estimated to be approximately
170 kilometers (106 miles), while in the eastern United States visual ranges are typically 25
km (15 miles) or less.
Figure 7 shows a more accurate depiction of median visual range in the western United
States based on early teleradiometer data. These data are for the summer of 1983. Note
that Grand Canyon median summer visual range was 172 km (107 miles). However, Grand
Canyon visibility was not as good as the 200-km (124-mi) visual range observed in Nevada
and Idaho (Lehman Caves and Craters of the Moon). On the other hand, visual range in
the Grand Canyon was much better than that observed to the west in southern California
(98 km) and to the east in Texas (103 km). Even lower visual ranges occurred in Los
Angeles and the southern San Joaquin Valley in California and further east in Texas.
One might therefore expect that, if winds were such that air from southern California or
Texas was transported to Grand Canyon, visibility would be poor, and if winds were from
Nevada, visibility would be good. Later chapters cite studies that show precisely this
relationship.
Visual range at the Grand Canyon varies over time with changes from day to day depending
on the nature of the air mass that is transported into the canyon. Appendix A shows
transmissometer measurements of visual range at the Grand Canyon over the past four
years. Note from the time series plots at the top of each page in this appendix that visibility
can vary dramatically from one day to the next. Some of this variation is natural, due to
changing relative humidity and because of cloud and fog. However, much of this variation
is due to anthropogenic sources (the influence of man's activities).
This temporal variation in visual range can be described by visual range statistics. In this
report, we use various visual range percentiles to characterize the temporal changes in visual
21
-------
Figure 6. Median visual range (in kilometers) in the United States in the late 1970s.
Source: Adapted from Trijonis and Shapland (1979) by Latimer and Ireson (1980).
-------
871
Olympic
%
90-110km
110-130km
130-150km
150-170km
170-190km
190-210km
NPS Monitors
Shenandoah 19km
H nro monitors
A Data Collected by Individual Park or State
Figure 7. Median summer visual range in the national parks in the western United States
in summer 1983. Source: National Park Service (1988).
23
-------
range. Table 1 and Figure 8 show the 10th, 50th, and 90t:h percentiles of visual range for
the period from the winter of 1987 through the spring of 1991. These statistics are for
periods exclusive of weather-related visibility impairment (precipitation and fog). The 50th
percentile, or median, visual range has been used in Figures 6 and 7. If all the measured
visual ranges are ordered from worst to best, the median visual range would correspond to
the middle of the distribution: 50 percent would have lower visual range, 50 percent higher.
Similarly, the 10th percentile is the visual range that is better than measured conditions 10
percent of the time, and the 90th percentile is the visual range that is better than measured
conditions 90 percent of the time.
Note from Figure 8 that the 10th percentile seasonal visual range at the Grand Canyon is
generally in the range from 100 to 150 km (62 to 93 miles).. The median is in the range 150
to 250 km (93 to 155 miles). The 90th percentile is in the range 200 to 350 km (124 to 217
miles). The mean of the median (50th percentile) visual ranges over the last four years at
Grand Canyon in 189 km (117 miles). The 4-year average 10th and 90th percentiles are 122
km (76 mi) and 277 km (172 mi), respectively.
Figure 8 shows that the highest visual ranges tend to occur in the fall and winter seasons
and the lowest visual ranges in the spring and summer. Also note that there is considerable
variation in seasonal visual range from one season to the next.
Figures 9 a through d show separately the transmissometer measurements for the winter,
spring, summer, and fall seasons, respectively. Note there is considerable variation in visual
range for a given season from one year to the next. For example, the median winter visual
range in 1988 was 267 km, while it was only 158 km in 1989. This is an extraordinary 41
decrease from 1988 to 1989. The median summer visual range in 1987 was 199 km, but
only 127 km in 1988 (a 36 percent decrease). These variations suggest that season-to-
season and year-to-year natural variations in meteorological conditions can cause
considerable variations in visual range. Thus, one cannot use a limited sample of visual
range statistics to determine trends. It is best to measure trends by comparing multiple-year
averages.
24
-------
Table 1. 10th, 50th, and 90th percentile visual range measured by transmissometer at the
south rim of the Grand Canyon, 1987 through 1991. Periods of weather (precipitation and
fog) are excluded. Source: Air Resource Specialists (personal communication, 1991).
Transmissometer Data for Grand Canyon
Visual Range (km)
Year
1987
1988
1989
1990
1991
Mean Visual Range
Last 4 Yrs
Winter
Spring
Summer
Fall
Season
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
10%-ile !
136
136
101
172
106
91
83
109
112
112
146
152
91
112
152
158
119
122
145
107
113
121
50%-ile !
189
199
189
267
165
127
146
158
158
165
235
250
165
180
235
209
172
189
215
165
168
201
30%-ile
286
286
285
367
309
180
209
199
250
250
335
335
267
286
309
309
250
277
299
269
251
285
25
-------
Visual Range at Grand Canyon
Transmissometer; Excluding Weather
0)
0)
CO
DC
S 150
W:87 Su:87 W:88 Su:88 W:89 Su:89 W:90 Su:90 W:91
Season from Winter 1987 to Spring 1991
10 Percentile
50 Percentile -*- 90 Percentile
Figure 8. 10th, 50th, and 90th percentile visual range measured by transmissometer at the
south rim of the Grand Canyon, 1987 through 1991. Periods of weather (precipitation and
fog) are excluded. Source: Air Resource Specialists (personal communication, 1991).
26
-------
Grand Canyon Visual Range in Winter
Transmissometer; Excluding Weather
400
1987
1991
10 Percentile
50 Percentile
90 Percentile
Figure 9. Yearly variation in seasonal visual range measured in the Grand Canyon, 1987
through 1991. Periods of weather (precipitation and fog) are excluded.
Figure 9(a) Winter.
27
-------
Grand Canyon Visual Range in Spring
Transmissometer; Excluding Weather
400
350
^ 300
E
Hr 25°
D)
S 200
cc
§ 150
100
50
1988
1989
1990
1991
Year
10 Percentile
50 Percentile
90 Percentile
Figure 9(b) Spring.
28
-------
Grand Canyon Visual Range in Summer
Transmissometer; Excluding Weather
400
1987
1988 1989
Year
1990
10 Percentile
50 Percentile
90 Percentile
Figure 9(c) Summer.
29
-------
Grand Canyon Visual Range in Fall
Transmissometer; Excluding Weather
400
350
^ 300
~ 250
CD
O)
g 200
DC
150
100
50
1987
1988 1989
Year
1990
10 Percentile
50 Percentile
90 Percentile
Figure 9(d) Fall.
30
-------
4. COMPOSITION OF GRAND CANYON HAZE
In the previous chapter we considered the recent measurements of visual range in the Grand
Canyon. We found that visual range in the Grand Canyon is considerably better than most
other places in the Lower 48 of the United States. We also found that visual range varied
from day to day, season to season, and year to year.
In this chapter we relate visual range to the particles that scatter and absorb light. We look
at the spatial and temporal variations in particle concentrations and chemical composition
at the Grand Canyon and how these variations can explain measured visibility.
Figures 10, a through g, show the measured fine particle concentration and chemical
composition throughout the IMPROVE network for the two-year period, December 1987
through November 1989. Note that fine particle concentrations are highest in the eastern
United States and in southern California. The fine particle concentration at the Grand
Canyon 3.271 n g/m3 is among the lowest measured in the Lower 48. (Note that the
concentrations in Figure 10 are shown in nanograms per cubic meter, ng/m3; these numbers
should be divided by 1000 to obtain micrograms per cubic meter, n g/m3.) The
concentrations of individual chemical constituents of the total fine particle mass at the
Grand Canyon are also among the lowest in the country.
The average concentrations of fine particle constituents at the Grand Canyon are as follows:
Ammonium sulfate [(NH4)2SO4] 0.91 n g/m3
Organics 0.75
Nitrates (NO30 0.20
Light Absorbing Carbon 0.14
Soil 0.70
Figure 11 shows the fractional contributions of these species to the total average fine
particle concentration at the Grand Canyon. Note that sulfate (21%), organics (23%), and
soil (24%) are the largest contributors to the fine particle mass, followed by light absorbing
31
-------
MEASURED FINE MASS NG/M**3
N)
/ //'5747Y
8168. V/
IMPROVE NETWORK 12/87 - 11/89
Figure 10. Mean fine particle concentrations in the United States from the IMPROVE
particle network over the period December 1987 through November 1989. Concentrations
are plotted in nanograms per cubic meter: divide these numbers by 1000 to obtain n g/m3.
Source: University of California, Davis and National Park Service (personal communication,
1990).
Figure 10(a) Total Fine Particle Mass.
-------
AMMONIUM SULFATE NG/M**3
•.2492.
IMPROVE NETWORK 12/87 - 11/89
Figure 10(b) Ammonium sulfate.
-------
QRGANICS BY TOR NG/M**3
•.2MO. -V :
IMPROVE NETWORK 12/87 - 11/89
Figure 10(c) Organics.
-------
NITRATES NG/M**3
IMPROVE NETWORK 12/87 - 11/89
Figure 10(d) Nitrates.
-------
LIGHT ABSORBING CARBON NG/M**3
IMPROVE NETWORK 12/87 - 11/89
Figure 10(e) Light Absorbing Carbon (LAC).
-------
FINE SOIL NG/M**3
IMPROVE NETWORK 12/87 - 11/89
Figure 10(f) Soil.
-------
UNEXPLAINED FINE MASS NG/M**3
oo
IMPROVE NETWORK 12/87 - 11/89
Figure 10(g) Unexplained Fine Mass.
-------
Chemical Composition of Haze
Grand Canyon, 1988 -1990, IMPROVE Data
Unaccounted (14.2%)
Soil (23.7%)
-SO4= (21.0%)
NO3- (4.4%)
Organics (22.9%)
LAC (13.8%)
Figure 11. Fractional contribution by mass of various chemical constituents of fine particles
measured in the Grand Canyon, December 1987 through November 1989.
39
-------
carbon (elemental carbon or soot) and other, unaccounted particles, both contributing 14
percent each. As will be shown below, since a larger fraction of sulfate appears to be
anthropogenic (man-caused) than either organics or soil and since sulfate has a higher light
scattering efficiency, sulfate is a proportionately larger contributor to haze in the Grand
Canyon than organics or soil.
It is interesting to compare the composition of the haze in this 1988-1990 period with that
in the measurement period 1982-1983 (see Figure 12). In the early 1980s sulfate accounted
for 36 percent of the fine particle mass in the Grand Canyon, while in the late 1980s it was
only 21 percent. This was a 42 percent reduction in less than 10 years. As discussed in a
later chapter, this decrease appears to be due to a huge reduction in regional sulfur dioxide
emissions due to the control and shutdown of copper smelters and sources in California.
Fine particle concentrations and chemical composition are measured for two 24-hour
periods every week at the Grand Canyon. Figures 13 and 14 show time series plots of these
measurements over the period 1988 - 1990. Note that particle concentrations varied
considerably over this two-year period. Highest concentrations occurred during the summers
of 1989 and 1990, and the lowest concentrations occurred during the winter of 1990. Figure
14 shows that sulfate and organics are generally the largest components of fine particle mass
in the Grand Canyon. The quite high fine particle concentration spikes (above 4 M g/m3)
appear to have high soil components, probably an indication of wind-blown dust.
The chemical composition values can be converted to a light extinction budget by
multiplying the concentrations of individual chemical species by their respective light
extinction efficiencies. This is done in the spreadsheet shown in Table 2. Fine particles are
broken down into sulfates and nitrates (plus water associated with these hygroscopic
species), organics, elemental carbon (light absorbing carbon or LAC or soot), soil dust, and
other fine particles. In addition, coarse particles and Rayleigh scattering (due to clean,
particle-free air) are added.
The table shows the light extinction efficiency (in m2/g) for each species. These values were
taken from the NAPAP Visibility State of the Science/Technology report by Trijonis et al.
(1989). The next column shows estimates by Trijonis et al. (1989) of natural background
concentrations in the rural West. It is assumed here that these average concentrations
would remain in the Grand Canyon even if all anthropogenic sources were controlled. The
next column represents the product of the first two, the light extinction contributed by each
species.
40
-------
Fine Mass
Sulfate
20 30 40
(a)
Remaining Mass
40
30
(c)
Soot
10
Figure 12. Fractional contribution by mass of various chemical constituents of fine particle
mass in the western United States during 1982 through 1983. Fine particle concentrations
(a) are in n g/m3 and species contributions (a - e) are in percent. Source: Malm (1989).
41
-------
Fine Particle Mass in Grand Canyon
IMPROVE Particle Data
62 139 216 293 11 88 168 242 315 24 101 174
Julian Day (1988-1990)
Figure 13. Time series plot of fine particle concentrations measured at the Grand Canyon
during 1988 - 1990.
42
-------
Fine Particles in Grand Canyon
Chemical Composition
62 139 216 293 11 88 168 242 315 24 101 174
Julian Day (1988-1990)
Sulfate ^| Nitrate
Absorbing Carbon WM Soil
Organics
Figure 14. Time series plot of fine particle chemical composition measured at the Grand
Canyon during 1988 - 1990.
43
-------
Table 2. Light extinction budget for the Grand Canyon in 1987-1989 based on IMPROVE
measurements of fine particle concentration and chemical composition and estimates by
Trijonis et al. (1989) of natural background concentrations and light extinction efficiencies.
The anthropogenic (man-caused) light extinction is the portion of total light extinction that
is not due to the natural background.
Natural Background
Existing Background
FINE PARTICLES
Sul fates plus Water
Organics
Elemental Carbon
Nitrate plus Water
Soil Dust
Other
COARSE PARTICLES
RAYLEIGH SCATTER
Efficiency Concentration
(m2/g) (ug/m3)
•
4.0 0.1
3.8 0.5
10.5 0.02
4.0 0.1
1.3 0.5
1.3 0
0.6 3
TOTAL LIGHT EXTINCTION
VISUAL RANGE (KM)
Bext
(Mm-1)
0.4
1.9
0.21
0.4
0.65
1.8
10
15.36
255
Concentration Bext
(ug/m3) (Mm-1)
0.66 2.63
0.75 2.87
0.15 1.53
0.20 0.80
0.70 0.91
0.37 0.48
3.00 1.80
10
21.03
186
Anthropogenic
Extinction
Delta Bext
(Mm-1)
2.23
0.97
1.32
0.40
0.26
0.48
0.00
0
5.67
-------
At the bottom of this column the visual range is calculated from the total light extinction
coefficient of 15.4 Mm"1 to be 255 km (158 mi). This visual range is an improvement of 35
percent from the current median visual range of 189 km (see Table 1). Thus, a 35 percent
improvement in average visual range in Grand Canyon is the upper bound of the practical
limit for visibility improvement. However, we need to emphasize that for specific episodes
visibility improvements greater than 35 percent are possible. For example, for the episode
measured on February 11, 1987 (see Figure 15), sulfates were 70 percent of total light
extinction. Thus, for this day a reduction of sulfate by 90 percent would improve visibility
by 170 percent.
The next columns refer to measured background particle concentrations that currently exist
in the Grand Canyon, as summarized above. The ammonium sulfate concentration referred
to earlier was reduced by the factor of 3/4.125 to represent sulfate mass only. The light
extinction translates to a visual range of 186 km, nearly equal to the current measured
Grand Canyon visual range of 189 km.
By subtracting the natural light extinction values from the existing background light
extinction values, we determine the anthropogenic (man-caused) light extinction in the
Grand Canyon. This is 5.7 Mm'1, or 27 percent of the total currently existing average light
extinction. Figure 16 shows the breakdown of this anthropogenic light extinction. Note that
organics are the largest single contributor at 31 percent, followed closely by sulfate at 29
percent. After organics and sulfate, major contributors are elemental or light absorbing
carbon or soot (17%), soil dust (10%), nitrate (9%), and other (5%).
Note that these numbers assume that two-thirds of existing organic particles are natural
(e.g., terpenes from vegetation). This is probably a fairly uncertain value. Until more
detailed studies are performed on the relative composition of natural and man-caused
organics at the Grand Canyon, the share played by organics of total anthropogenic light
extinction should be treated with caution.
To control organics will require the targeting primarily of motor vehicle emissions. To
control sulfate will require the targeting of fossil-fuel combustion sources (especially coal-
fired power plants) and copper smelters. To control elemental carbon will require the
targeting of diesel vehicles and residential wood combustion. Nitrate is contributed by the
combination of power plants and motor vehicles. Thus, Figure 16 provides a good overview
of the types of controls that will be necessary to improve visibility in the Grand Canyon.
45
-------
Other 11%
Sulfates 70%
Rayleigh 19%
Feb 11
Rayleigh 96%
Other 4%
Feb 14
Figure 15. Light extinction budgets for two days at the Grand Canyon in 1987 (Malm et al.,
1989).
46
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Anthropogenic Extinction
Grand Canyon National Park (1988-89)
Other (5.2%)-i
Soil Dust (9.9%)- ^
Nitrate (8.7%)
Elemental Carbon (16.6%)
Sulfate (28.5%)
Organics (31.0%)
Figure 16. Breakdown of anthropogenic (man-caused) light extinction in the Grand Canyon
in 1988-89 based on the light extinction budget shown in Table 2.
47
-------
If the individual measurements of fine particle chemical species plotted in Figure 14 are
multiplied by their respective light extinction efficiencies and summed and then divided into
3.9 to obtain visual range, we obtain the time series plot shown in Figure 17. Note that the
variations in calculated visual range track the variations in measured visual range shown
earlier.
48
-------
Visual Range in Grand Canyon
From Fine Particle Mass and Composition
400
IIIIIIIIIIIIINIIlllUiUfllHIllHHIJIUUIlUlUUIllUlHIIIIIIIIIIIIIIIIIIIIIIHMlW
62 139 216 293 11 88 168 242 315 24 101 174
Julian Day (1988-1990)
Figure 17. Time series of visual range for the Grand Canyon calculated from the twice
weekly measurements of fine particle concentration and chemical composition for 1988 -
1990.
49
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5. REGIONAL EMISSIONS OF HAZE PRECURSORS
We discussed the importance contributions of organics, sulfates, elemental carbon, and
nitrates to the anthropogenic haze observed in the Grand Canyon. Thus, to improve
visibility in the Grand Canyon, one needs to consider the control of sources of these
chemical species. This implies controlling sources of sulfur oxides (SOX which form sulfate),
nitrogen oxides (NOX which form nitrate), and reactive hydrocarbons (RHC which form
organics).
Figure 18 shows the distribution of SOX, NO^ and RHC sources in the early 1980s in the
western United States. Note that Texas is a major source of all three species; thus, one
would expect that when winds are from this direction, Texas may be a significant source of
Grand Canyon haze.
However, winds are not often from Texas. Figures 19 and 20 show that the most common
upper-air transport winds are from the southwest and west, not from the southeast (the
direction in which Texas is located). Appendix B shows wind fields for individual months
in 1981.
Aside from Texas, the dominant source of haze precursors appears to be California. NOX
and RHC emissions are quite high. SOX emissions are quite high, although not as high as
those in Arizona. Thus, we would expect California to be a significant contributor.
Figure 21 shows the breakdown of SOX and NOX emissions in 12 western states based on the
1983 NEDS emissions inventory.
For SO^ the dominant source in the early 1980s was copper smelters at 38 percent followed
by power plants (26%), other combustion sources (13%), the petroleum industry (8%), other
industry (8%), and motor vehicles (7%).
For NO^ the dominant source was motor vehicles at 50 percent, followed by power plants
(30%), other combustion (15%), and others (5%).
50
-------
rsno Scale
o in : kiloton/yr
£3 Point sources SOx
I Area sources SOx
Point sources NOx
Area sources NOx
Point sources RHC
Area sources RHC
Figure 18. Distribution of sources of sulfur oxides (SOX), nitrogen oxides (NOX), and
reactive hydrocarbons (RHC) in the western U.S. in the early 1980s, by state and by point
or area source. Source: Chinkin, Latimer, and Mahoney (1987).
51
-------
f - 2.b K/S
I i
J
e se tee
Parce-iage
Figure 19. Wind roses for upper-air (300 m agl) winds in 1964 throughout the western U.S.
(a) Morning.
52
-------
L
0 50 100
P«rcentaoe
(b) Afternoon
Figure 19 (concluded), (b) Afternoon.
53
-------
7
7 77 7 7 7 y
LLJjll // // V '
Figure 20. Mean resultant wind vectors for the mixed layer in 1981 in the
Southwest. Source: Latimer et al. (1985b).
-------
SOx Emissions by Source Category
Power Plants
Motor
Vehicles
Petroleum
Industry
Smelters
Other Combustion
Other Industry
NOx Emissions by Source Category
Power Plants
Motor
' Vehicles
Other S
Combustion
Petroleum / / xSmelters (1%)
other |ndustry
(2%)
(2%)
Figure 21. Source category breakdown of SOX and NOX emissions in the 12 western U.S.
states based on the 1983 NEDS emissions inventory. Source: Chinkin, Latimer, and
Mahoney (1987).
-------
For organics the dominant sources would be motor vehicles and industry in urban areas.
Figures 22, (a) and (b), show the approximate size and location of major SOX point sources
in the Southwest in 1981 and 1987, respectively. The areas of the circles are approximately
proportional to the estimated emission rates. Note that in 1981 SOX emissions were much
larger than in 1987. As we will show later in this report, there has been approximately a
50 percent reduction in SOX emissions in the Southwest during the past decade. The
reductions have occurred primarily from the closing and control of copper smelters, but also
from the control of many sources in California, and the 70 percent control at the Four
Corners Power Plant. Table 3 summarizes the largest SOX sources in the western United
States in the early 1980s. Note that many of these sources have been controlled or shut
down. The Navajo Generating Station, one of the largest sources in the Southwest, has
committed to reducing its SOX emissions by 90 percent by the end of this decade. Table 4
shows emissions from major point sources in 1987.
From the pattern of SOX source location shown in Figures 18 and 22 and from the prevailing
winds in Grand Canyon area shown in Figures 19 and 20, one would expect that (1)
southern California might be a frequent contributor to Grand Canyon haze, (2) haze would
worsen when the wind carries emissions from southern Arizona, northern Mexico, New
Mexico, and Texas to the Grand Canyon, and (3) visibility would improve when wind carries
air over the relatively clean Nevada desert. As we show in the next chapter, trajectory
analyses have confirmed this expectation.
56
-------
Tons/day of SO2
Figure 22. Estimated regional SO2 emissions in the Southwest in 1981 and 1987 based on
the estimates in Latimer (1990).
(a) 1981
57
-------
Ik-
Grand Canyon
National Park
Figure 22 (concluded).
(b) 1987
58
-------
Table 3. Largest SOX point sources in the western United States in the early 1980s, ranked
in descending order of emissions. Note that several of the largest sources in the early 1980s
have been shut down or controlled. Source: Chinkin, Latimer, and Mahoney (1987).
SOX Emissions (KTPY)
Facility
Phelps Dodge (Douglas)
Magma (San Manuel)
ASARCO (Tacoma)3
Kennecott (McGIll)2
Phelps Dodge (Morend)4
Four Corners
Navajo
Central 1a
Kennecott (Hayden)6
ASARCO (Hayden)
Kennecott (Garfleld)7
Kennecott (Hurley)
J1m Brldger
Phelps Dodge (Playas)
Phelps Dodge (Ajo)8
Leland Olds
ASARCO
Coal Creek
Mohave
Milton R. Young
Naughton
Dave Johnston
Inspiration (Miami)
Notes:
Type State Mean
S AZ 313.0
S AZ 153.6
S WA 142.7
S NV 113.9
S AZ 104.6
PP NM 69.0
PP AZ 66.2
PP WA 65.5
S AZ 64.2
S AZ 62.9
S UT 56.5
S NM 53.9
PP WY 48.0
S NM 41.9
S AZ 34.8
PP ND 34.6
M MT 31.4
PP ND 27.4
PP NV 27.5
PP ND 24.2
PP WY 23.5
PP WY 22.9
S AZ 20.8
Standard
Deviation
102.4
44.5
60.8
105.2
29.2
27.9
7.8
5.7
52.7
68.2
26.5
23.5
9.6
13.1
18.2
5.3
32.8
21.2
7.1
3.2
6.3
12.2
2.5
Standard
Deviation *
Mean
0.33
0.29
0.43
0.92
0.28
0.40
0.12
0.09
0.82
1.08
0.47
0.44
0.20
0.31
0.52
0.15
1.01
0.77
0.26
0.13
0.27
0.53
0.12
Control
Efficiency (X)
0
63
0
0
0
80
0
0
90
90
95
N/A
90 (Unit 4 only)
92
93
0
75
90
0
0
80 (Unit 3 only)
0
84
(1) Expected to shut down by 15 January 1987.
(2) Permanently closed
(3) Permanently closed
(4) Temporarily closed
1n June 1983.
1n March 1985.
for Installation of emission
controls.
(5) Emission controls were Installed 1n November 1984; current
(6) Suspended operation
(7) Suspended operation
(8) Temporarily closed
Indefinitely 1n May 1982.
Indefinitely.
for Installation of emission
controls.
S0y emissions
A
• 32 KTPY.
59
-------
Table 4. Regional emissions in tons per day in 1987. Source: Malm et al. (1989).
SITE
LOCATION
NOX PART
Apache
Coronado
NGS
Springerville
Cholla
Cameo
Craig
Hay den
Escalante
Four Corners
San Juan
North Valmy
Mohave
Carbon
Hunter
Huntington
Bridger
Naughton
Asarco-Hayden
Inspiration
Magma
Nacozari
Cananea
Cohise, AZ
St. Johns, AZ
Page, AZ
Springerville, AZ
Joseph City, AZ
Grand Junction, CO
Craig, CO
Hayden, CO
Preuritt, NM
Fruitland, NM
Waterflow, NM
Valmy, NV
Mohave, NV
Castledale, UT
Castledale, UT
Huntington, UT
Point of Rocks, WY
Kemmerer, WY
Hayden, AZ
Miami, AZ
San Manuel, AZ
Nacozari, Sonora
Cananea, Sonora
LA/Southern CA
Phoenix, AZ
Las Vegas, NV
El Paso, TX
Salt Lake City,UT
5.5
17.5
163
13.2
44.7
8.2
24.4
40.5
2.7
105.5
115.6
11.5
51.5
15.6
15.9
32.6
145.9
41.4
92
54
480
380
240
+
*
*
*
*
7.4
19.2
73
10.1
35.9
9.6
38.4
23.0
7.9
227.7
93.2
15.9
42.2
12.0
53.4
58.1
90.1
40.3
*
*
*
*
*
*
*
*
*
*
3.8
.27
6.4
.5
4.4
*
*
*
.4
2.8
*
3.3
*
*
*
*
12.3
*
*
*
*
*
*
*
*
*
*
*
60
-------
6. SOURCE REGION CONTRIBUTIONS
TO GRAND CANYON HAZE
By plotting the trajectory taken by an air mass that arrives at the Grand Canyon, one can
determine the probable source regions of haze.
Figures 23 and 24 show trajectories associated with high and low visibilities at the Grand
Canyon. The highest visual ranges are associated with transport from Nevada, Oregon,
Idaho, and Utah, and high winds from California. The lowest visual ranges are associated
with transport from southern California, southern Arizona, New Mexico, and Texas. This
work led to the coining of the term, "clean air corridor," by Pitchford et al. (1981). This
term means the area north and west of the Grand Canyon, that is a source of clean, clear
air.
The use of trajectory analysis was further refined by Bresch et al. (1984), Ashbaugh, Malm,
and Sadeh (1985), and Malm, Johnson, and Bresch (1986). These workers developed a
technique of calculating a conditional probability, given the air originated from a given area,
that particle concentrations would be high or low at the Grand Canyon.
Figures 25, (a) and (b), show the conditional probabilities of high and low sulfate
concentrations for the Grand Canyon in 1980 -1981. Note that if air passed over southern
Arizona and northern Mexico on its way to the Grand Canyon, the probability would be
over 50 percent that sulfate concentrations would be elevated at the Grand Canyon. This
is precisely the region occupied by the Douglas and Cananea smelters, two of the largest
SOX sources in the early 1980s. Other areas with high probabilities include New Mexico,
Texas, Los Angeles, and the south San Joaquin Valley in California, all known SOX source
areas. Conversely, the areas with high probabilities of low sulfate concentrations at the
Grand Canyon point largely to the north, to Nevada, southern Utah, and Colorado, locations
where there are few significant SOX sources, the "clean air corridor."
The technique has recently been applied again to concentrations in the latter 1980s by
Gebhart and Malm (1991). Figure 26 shows the source contribution functions for high and
low sulfate concentrations in Grand Canyon for the period June 1984 through December
61
-------
Regional analysis of faciors affecting visual air quality
• Cities
O Length ot Trajectory in Hours
Wind irajectoncs hack in time for days of high visual air quality.
• Cities
O Length of Trajectory in Hours
Wind trajectories back in time for days of low visual air quality.
Figure 23, Back trajectories from the Grand Canyon in 1978 and 1979 associated with high
and low visual air quality. Source: Pitchford et al. (1981).
62
-------
Figure 24. Back trajectories from the Grand Canyon on two hazy days and one clear day
in the Grand Canyon in 1979. Source: Macias, Zwicker, and White (1981).
63
-------
4I°N
HIGH CONCENTRATION CONDITIONAL PROBABILITY
SEPTEMBER 1980-AUGUST 1981
GRAND CANYON, AZ
30°N
II9°W
|I4°W
LONGITUDE
I09°W
Figure 25. Conditional probabilities that given an air parcel at the Grand Canyon originates
from the indicated area it will be associated with high or low sulfate concentrations,
September 1980 - August 1981. Source: Bresch et al. (1984).
(a) High Concentration Conditional Probability
-------
cr\
LOW CONCENTRATION CONDITIONAL PROBABILITY
SEPTEMBER 1980 - AUGUST 1981
GRAND CANYON, AZ
4I6N
30°N
124° W
II9°W
II4°W
LONGITUDE
I09°W
104° W
Figure 25 (concluded).
(b) Low Concentration Conditional Probability
-------
GRAND CANYON NATIONAL PARK
ON
\
90.00
»tM;;iN{;»jj
"f;f;::»*it;:
;::::::i;::::::
70.00
50.00
30.00
Figure 26. High and low concentration source contribution functions for Grand Canyon
sulfate, June 1984 - December 1989. Source: Gebhart, personal communication (1991).
10.00
(a) High-concentration Source Contribution Function (HSCF).
-------
GRAND CANYON NATIONAL PARK
**•• "*•». »«_L' "
Figure 26 (concluded).
J»0,00H
70.00
50.00
30.00
10.00
(b) Low-concentration Source Contribution Function.
-------
1989. The source contribution function accounts for the probability of transport winds from
various directions. It points to the source areas that most frequently cause high or low
concentrations. Since winds are more frequently from the southwest than from the south
(the smelter region) or from the southeast (Texas), the high concentration source
contribution map points more to sources in Los Angeles and the San Joaquin Valley in
California and less to the smelter area of southern Arizona and northern Mexico. Other
source areas of high Grand Canyon sulfate also appears to be Salt Lake City and western
Wyoming where a large smelter and power plant, respectively, are located. Figure 27 shows
the source contribution function map for light absorbing carbon and organic carbon. Note
surprisingly, these maps target the urban areas of California.
Figure 28, (a) and (b), shows the conditional probability plots for high and low sulfate
concentrations, respectively, for the late 1980s for comparison with Figure 25 for the early
1980s. With the control of copper smelter emissions during the last decade, Figure 28 points
more to Texas, with its high SO2 emissions, than to southern Arizona and northern Mexico.
Figure 29 summarizes the results of these analyses. Most often dirty air is transported to
the Grand Canyon from the urban and industrial areas of southern California and the San
Joaquin Valley. At times dirty air is transported to the Grand Canyon from southern
Arizona, northern Mexico, New Mexico, and Texas where copper smelters and power plants
are located. At other times relatively clean air is transported to the Grand Canyon from
the "clean air corridor" to the north.
68
-------
90.00
;}!»»»!««.
iu^"j*j?jrs?!
70.00
50.00
30.00
10.00
(a) Light Absorbing Carbon.
98.BB
70.00
50.00
30.00
10.00
(b) Organic Carbon.
Figure 27. High-concentration source contribution functions for Grand Canyon light
absorbing carbon and organic carbon, June 1984 - December 1989. Source: Gebhart et al.
(1991).
69
-------
GRAND CANYON NATIONAL PARK
60.00
Figure 28. Conditional probabilities that given an air parcel at the Grand Canyon originates
from the indicated area it will be associated with high or low sulfate concentrations, June
1984 - December 1989. Source: Gebhart, personal communication (1991).
50.00
40.00
30.00
20.00
(a) High Concentration Conditional Probability
-------
GRAND CANYON NATIONAL PARK
*x
60.00
50. 00
40.00
30.00
Figure 28 (concluded).
(b) Low Concentration Conditional Probability
20.00
-------
'Clean Air Under
Some Meteorological Conditions
Grand Canyon
Development
Copper,^?
Smelters
Dirty Air Under Some
Meteorological Conditions
Dirty Air Most Off*
*> \ ••••::•••• •':;«:
*~\ -;3
' * Urban -'v
Figure 29. Summary of current understanding of the sources and frequency of transport of
clean and dirty air in the Grand Canyon. Source: NFS (1988).
72
-------
7. SPECIFIC EMISSION SOURCE CONTRIBUTIONS
TO GRAND CANYON HAZE
The analyses described in the previous chapter identified source regions contributing to high
sulfate concentrations at the Grand Canyon. In this chapter, we discuss two analysis
approaches that identified specific emission sources (e. g., individual power plants, smelters,
and urban areas).
BACK TRAJECTORY ANALYSIS
Iyer, Malm, and Ahlbrandt (1986) and Gebhart et al. (1988) have extended the back
trajectory source contribution function analysis described in the previous section by
incorporating information regarding the location of SOX emission sources.
Figures 30 and 31 summarize a set of estimates using this technique of the emission source
contributions to sulfate concentrations in the Grand Canyon for the periods 1980-84 and
1981-85. Note that in these analyses, southern California (including the San Joaquin Valley)
is identified as the single largest source of Grand Canyon sulfate haze (20-35%), followed
by smelters (10-15%), the Mohave Power Plant (7-15%), the Navajo Generating Station (1-
7%), and Salt Lake City (4-6%).
REGIONAL HAZE DETERMINISTIC MODELING
Latimer et al. (1985a) used a Lagrangian regional model and modeled all known sources
of SOX, NO^ organics, elemental carbon, and other particles in the Southwest based on
meteorological data for the year 1981. The model calculations of sulfate concentration and
light extinction were compared in detail with the available measurements in the Southwest
from EPA's Western Particle Characterization Study (WPCS) and the Electric Power
Research Institute's Western Regional Air Quality Study (WRAQS). Figures 32 and 33 and
Table 5 show the comparison of model calculations and measurements. The model was able
73
-------
1MO-1M4-Wlth Emtukm Dili. lnl.ic»pl FleMn«
1MO-11l»4-Wiin Em.11.on Oilv InMMfM FuM A1 Z»o
S. C4lilon>i» 10 4%
( CMilomx J4.«%
S«n Fr»nci»co 5.0%
Ely. Nevaoj 6.7%
AZ. Smellirt u ?
1980-1»M-No EmbtlOfi Dill, Inltrnpl Ftolllng
1MO-1M4-Ne Enyulon O«U. kiMrupt Flitd A1 Zm
S COi'omn J4 7%
AZ Smen>n14IN
Figure 30. Source contributions to sulfate in the Grand Canyon estimated based on back
trajectory analysis, 1980 - 1984. Source: Gebhart et al. (1988).
74
-------
Sources of Sulfate at Grand Canyon
San Francisco
4%
Ely, Nevada
6%
Salt Lake City
5%
Colorado
1%
Albuquerque
2%
Four Corners
1%
New Mexico
El Paso
2%
New Mexico
Smelters
2%
Refineries
5%
Southern California
33%
Arizona
Smelters
15%
Mexico
3%
Navajo
7%
Mojave
14%
Figure 31. Source contributions to sulfate in the Grand Canyon estimated based on back
trajectory analysis, 1981 - 1985. Source: Malm (1989).
75
-------
FIGURE 1. Comparison of predicted and measured annual average S04 (ug/m).
(Values measured at WPCS sites are shown in solid boxes, values
measured at WRAQS sites are shown in dashed boxes.)
4. OS
3.0D
- 2.00
1.0!
1.00 2.00 5.CO
OBSERVED IUG/K3I
Figure 32. Comparison of regional model calculations and measurements of annual average
sulfate concentrations in the Southwest in 1981. Source: Latimer et al. (1985b).
76
-------
; 32.8;
FIGURE 3. Comparison of predicted and measured annual averaqe linht extinction
(l/f*n). (Values measured by teleradioneter at NPS sites are shown in solid boxes.
values measured at WRAOS sites ire shown in dashed boxes.)
- 3C.OO
15. 00
15.00 30.00 45.00 60.00
OBSERVED Il/Hil
Figure 33. Comparison of regional model calculations and measurements of annual average
light extinction coefficient (bext) in the Southwest in 1981. Source: Latimer et al. (19855).
77
-------
TABLE I. Comparison of model calculations with measurements.
Modeled or Measured Quantity
Sulfate Concentration
Annual average
Monthly average
Seasonal averages
Uinter
Spring
Summer
Fall
Light Extinction Coefficient (bext)
Annual average
Monthly average
Seasonal averages
Winter
Spring
Summer
Fall
Bias1
-0.03
0.00
-0.60
-0.10
+0.41
-0.21
+0.03
+0.06
+0.18
+0.02
-0.03
+0.08
Correlation 2(r)
0.733
0.50
0.67
(1.47
0.84
0.75
0.404'5
0.31
-0.03
0.375
0.525
0.245
Particle Light Scattering Coefficient (bs_)
Annual average +0.35 0.66
Monthly average +0.27 0.44
Wet Sulfur Deposition
Annual +0.45 0.69
Monthly +0.51 0.47
Miscellaneous Aerosol Concentrations
Nitrate -0.12
Organics -0.61
Elemental carbon (soot) -0.57
Bias « (Predicted/measured) -1; over- and underprediction are
positive or negative values, respectively; perfect bias is zero.
For perfect correlation, r = 1.0; with no correlation, r = 0.0; r^ =
fraction of observed variance explained by model.
3 For comparison, sulfate measurements within 150 km of each other
correlated at r « 0.54.
For comparison, bext measurements within 150 km of each other
correlated at r • 0.31.
5 With outliers removed, r >0.8.
Table 5. Comparison of regional model calculation and measurements of regional air
quality, visibility, and deposition in the Southwest in 1981. Source: Latimer et al. (1985b).
78
-------
to reproduce the measured annual average sulfate and light extinction with virtually no bias
with correlations of 0.73 and 0.40, respectively.
Figure 34 shows the results of the regional haze model calculations of the contributions of
both sources and chemical species to annual average anthropogenic light extinction in the
Grand Canyon in 1981. Note that in this study sulfate was found to be the largest single
man-made contributor to Grand Canyon haze in 1981 at 59 percent of total anthropogenic
extinction. Sulfate was followed by other fine particles (16%), nitrate (11%), coarse
particles (7%), soot (6%), and organics (1%). The man-made organic contribution was so
small in this study because primary organics were lumped into the fine particle category.
The source categories contributing to Grand Canyon haze included power plants, the
petroleum industry (largely in the San Joaquin Valley in California and in Los Angeles),
copper smelters (largely in southern Arizona and northern Mexico), motor vehicles, fugitive
dust sources, and transport from outside the region (e.g., Texas and the Pacific Northwest).
Each category was found to contribute approximately equally with 10 to 18 percent of the
man-made haze. Prescribed fires were only 5 percent of the total anthropogenic light
extinction but 68 percent of the elemental carbon haze.
Figure 35 shows the breakdown of source category contributions to the sulfate portion of
the anthropogenic haze. This graph is shown so that these results can be compared to the
results shown above from the back trajectory analysis. Note that at total of 43 percent of
Grand Canyon sulfate is predicted to originate from the oil industry (28%), vehicles (9%),
other industry (5%), and residential/commercial sources (2%). Most of these source
categories are located in California. Therefore, this analysis concludes that approximately
40 percent of Grand Canyon haze, the largest single share, originates in California. This
compares reasonably well with the back trajectory analysis cited above. The next largest
category was found to be copper smelters (26% of sulfate in 1981), transport from outside
the Southwest (e.g., from Texas; 17%), and power plants (14%).
Figure 36 shows a source breakdown of the copper smelter portion (26%) of the sulfate
haze portion (59%) of Grand Canyon haze. Note that the most significant contributor in
1981 was the Douglas smelter (22%), which has since been shut down. The McGill smelter,
also a large contributor in the early 1980s has also been shut down. Other smelters, like
Morenci, have since been controlled. Now, the San Manuel, Arizona and Cananea, Mexico
smelters are the largest smelters still operating.
Figure 37 shows a source breakdown of the power plant portion (14%) of the sulfate haze
portion (59%) of Grand Canyon haze. Note that the single largest contributor was the
79
-------
16.52 Smelters
16.9X 011 Industry
3.5X Other Industrial
1. 3X Resident!al/Comm
4.3X Gasoline Vehicles
2.8X Diesel Vehicles
3.7X Other Mobile
0.3X Evaporative
18.4X Utilities
10.3X Extraregional Trans
0.1X Wood Stoves/F.P.
5.4X Pres Burns/F.F.
16.62 Fugitive Dust
(a) by source category
59.3X S04
10.92 N03
0.9X Or§agj,cSoot
0.1X N02
6.9X Coarse PM-10
16.3X Other PM-2.5
(b) by species
Figure 34. Emission source category and chemical species contributions to annual average
anthropogenic light extinction in the Grand Canyon in 1981, based on regional model
calculations. Source: Latimer et al. (1985a).
80
-------
Source Contributions in 1981
to Grand Canyon Sulfate Haze
Outside Region (17.4%)
Vehicles (8.6%)
Res./Comm. (1.5%
Other industrial (4.6*
F3ower Plants (14.0%)
Smelters (25.7%)
Oil Industry (28.1%)
Figure 35. Breakdown of the emission source category contributions to the sulfate portion
of the anthropogenic light extinction in the Grand Canyon in 1981, based on regional model
calculations.
81
-------
Smelter Contributions in 1981
to Smelter Portion of Sulfate Haze
Others (9.5%)
Garfield (3.7%)
Hayden (As.) (4.6%)
Hayden (Ken.) (7.0%)
El Paso (2.8%)
Cananea (7.2%)
Morenci (10.1%)
Douglas (22.1%)
San Manuel (20.0%)
Figure 36. Breakdown of the copper smelter source contribution to the sulfate portion of
the anthropogenic light extinction in the Grand Canyon in 1981, based on regional model
calculations.
82
-------
Power Plant Contributions in 1981
to Power Plant Portion of Sulfate Haze
Urban Plants (42.5%)
Others (7.8%)
Four Corners (9.9%)
Navajo(12.1%)
Jim Bridger (3.6%)
Mohave (22.4%)
Coronado (1.7%)
Figure 37. Breakdown of the power plant source contribution to the sulfate portion of the
anthropogenic light extinction in the Grand Canyon in 1981, based on regional model
calculations.
83
-------
Mohave Power Plant (22%) in Bullhead City, Nevada, near and in the prevailing upwind
direction from Grand Canyon. Mohave was followed by Navajo (12%), which will be
controlled by 1999, Four Corners (10%), which has been since controlled, and Jim Bridger
(4%) in Wyoming, an uncontrolled coal-fired power plant. Several urban power plants
(located in southern California) combined contributed 43 percent of the power plant fraction
of sulfate haze. Much of these emissions have been reduced since the early 1980s.
Considering the past and future controls on the California power plants, Four Corners, and
Navajo, Mohave and Jim Bridger stand out as significant power plant sources of Grand
Canyon sulfate.
Figure 38 shows a breakdown of the urban area contributions (43%) to the sulfate portion
(59%) of Grand Canyon haze. Note that southern California (Los Angeles, San Joaquin
Valley, and San Diego) are by far the largest urban contributors (84%) to sulfate in the
Grand Canyon. The San Francisco Bay Area contributes 13 percent. Considering the
significant controls on SOX emissions in the Los Angeles area, the San Joaquin oil industry
currently stands out as a significant contributor to Grand Canyon sulfate.
These results are in qualitative agreement with the results of the back trajectory analyses
presented in this chapter and the previous chapter.
Figure 39 shows a breakdown of the urban area contributions to organics in the Grand
Canyon. Here California sources dominate (51%), but Arizona sources, including Phoenix
and Tucson, are large contributors (34%), as is Las Vegas (8%). If man-made organics are
found to be a significant fraction of Grand Canyon haze, controls on vehicular emissions
throughout major urban areas of the Southwest may be necessary to improve Grand Canyon
visibility.
SIMPLE SOURCE-RECEPTOR RELATIONSHIPS
One would expect that an emission source would have a proportionately larger impact on
a receptor if it is close and generally upwind. Empirical and theoretical studies have
confirmed this relationship.
Marians and Trijonis (1979) found a nearly inverse relationship between regional sulfate
concentrations and source-receptor distance (see Figure 40):
-0.95
y dor >
84
-------
Urban Area Contributions in 1981
to Urban Area Portion of S5ulfate Haze
San Francisco (13.2%)
Others (2.4%)
San Joaquin (33.8%)
San Diego (2.3%)
Los Angeles (48.3%)
Figure 38. Breakdown of the urban area source contribution to the sulfate portion of the
anthropogenic light extinction in the Grand Canyon in 1981, based on regional model
calculations.
85
-------
Urban Area Contributions in 1981
to Urban Area Portion of Organics Haze
Phoenix (32.2%)
Tucson (2.2%)
Others (6.7%)
San Diego (3.3%)
Los Angeles (24.4%)
Las Vegas (7.8%)
San Joaquin (7.8%)
San Francisco (7.8%)
Sacramento (7.8%)
Figure 39. Breakdown of the urban area source contribution to the organic carbon portion
of the anthropogenic light extinction in the Grand Canyon in 1981, based on regional model
calculations.
86
-------
.4
.3 _
4
.2 _
0
o.
E
O
.1
.08
.06
.05
.04,
.03
.02_
s
IS)
^H
s:
o
.01-
.008'
.006
.005
.004 _
.003-
.002-
.001
10
y = 1.66x"'95
Correlation Coefficient » -0.83
• Based on airport data
O Based on NASN sulfate data
Note: Farmington data excluded because
of negative extinction/emission
change.
I I I I I i I
20 30 40 50 60 80 100
X, DISTANCE TO SMELTERS (miles)
I
200
300 400
Figure 40. Inverse relationship between sulfate light extinction and distance from the
copper smelters to a receptor. Source: Marians and Trijonis (1979).
87
-------
where y is the sulfate concentration at a receptor, a0 is the SOX emission rate, and r is the
distance from the emission source to the receptor.
Chinkin, Latimer, and Mahoney (1987) and Daly et al. (1988) found inverse relationships
between acid deposition and source-receptor distance.
Latimer (1990) derived a somewhat more complex relationship. He found that there was
an inverse relationship between sulfate concentration and source-receptor distance.
However, the exponent k varied depending on the wind speed, mixing height, and SO2
oxidation rate:
= a0
where CS04 is the ambient sulfate concentration downwind, a0 is a constant, QS02 is the
source's SO2 emission rate, r is the downwind distance between source and receptor, and
the exponent k was found also to be a function of distance r:
k = 0.2 + aj r ,
where al varies depending on the degree of stagnation. The values of ^ were found to be
2.75, 0.90, 0.22, 0.29, and 0.40 10-3/km.
The latter relationships were programmed into a spreadsheet. Table 6 shows the results for
estimated regional SO2 emissions in 1981 and 1987. Note first that between 1981 and 1987
regional SO2 emissions were reduced 53 percent from 5700 to 2700 tons per day. The last
four columns in Table 6 show the estimated source contributions to Grand Canyon sulfate
in each season. Note that the Navajo Generating Station was found to dominate winter
impacts because of stagnation. However, in the other seasons the dominant sources of
Grand Canyon sulfate were the San Manuel, Arizona smelter (32%), the Nacozari and
Cananea, Mexico smelters (13% and 10%), Mohave and Navajo (each at 5%), and Los
Angeles and San Joaquin Valley (each at 4%).
CONTRIBUTIONS OF THE NAVAJO GENERATING STATION
The impact of the Navajo Generating Station (NGS) on Grand Canyon haze in the winter
has been the subject of several major studies in recent years. The National Park Service
conducted the WHITEX study (Malm et al., 1989) in the winter of 1987 that suggested that
NGS contributes about 70 percent, on average, during certain episodes to the sulfate
-------
Table 6. Source contributions to ambient sulfate in the Grand Canyon in 1981 and 1987,
based on simple source-receptor relationships. Source: Latimer (1990).
oo
S02 Emission Nate
(tons/day)
Emissions
Power Plants
Copper Smelters
California
Sources
K>
Source
Navajo
Choi la
Mohave
Coronado
Sprlngarvlll*
San Juan
Four Corner*
Hunting ton Canyon
Hunter
Carbon
Cameo
Hayden
Craig
Jim Brldger
Naughton
Apache
Morth Valmy
Douglas
San Manuel
McGIll
Morencl
Hayden
Garfield
Hurley
Playas
Ajo
Miami
Cananea
Nacozari
Los Angeles
San Diego
Santa Barbara
San Joaquln Valley
San Francisco
Sacramento
TOTAL
Percentage Change
1981
153.00
7.00
79.00
27.00
0.00
77.00
257.00
25.00
0.00
16.00
8.00
33.00
16.00
126.00
52.00
6.00
0.00
910
595
345
337
364
219
173
126
107
79
301
0
592.00
55.00
44.00
241.00
304.00
11.00
5685
1987
163.00
45.00
52.00
18.00
13.00
116.00
106.00
33.00
16.00
16.00
8.00
41.00
24.00
146.00
41.00
6.00
12.00
0.00
480.00
0.00
0.00
92.00
0.00
110.00
82.00
0.00
54.00
240.00
380.00
110.00
27.00
22.00
110.00
110.00
5.00
2678.00
•52.9
Appro*.
Distance
(km) to
Grand Canyon
117.00
310.00
225.00
270.00
300.00
310.00
310.00
340.00
340.00
340.00
430.00
570.00
570.00
640.00
600.00
430.00
680.00
550.00
380.00
430.00
390.00
340.00
450.00
500.00
570.00
390.00
300.00
580.00
630.00
610.00
610.00
610.00
600.00
910.00
890.00
Upwind or Downwind of Grand Canyon?
(Upwind * 1; Downwind > 4)
Winter
1.00
1.00
4.00
1.00
4.00
1.00
1.00
.00
.00
.00
.00
.00
.00
.00
1.00
4.00
4.00
4.0Q
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
Spring
4.00
4.00
1.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
1,00
1.00
4.00
1.00
1.00
4.00
4.00
4.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Summer
4.00
4.00
1.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
1.00
1.00
4.00
1.00
1.00
4.00
4.00
4.00
1.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
Fall
4.00
4.00
1.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
1.00
1.00
4.00
1.00
1.00
4.00
4.00
4.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
1.00
Percentage Contribution of Given Source
to Ambient Sulfate Concentration In 1981
Winter
56.55
0.39
2.57
2.26
0.00
4.34
14.49
1.05
0.00
0.67
0.13
0.13
0.06
0.24
0.15
0.03
0.00
1.36
4.16
1.45
2.13
3.80
0.75
0.35
0.12
0.67
1.24
0.27
0.00
0.38
0.04
0.03
0.17
0.01
0.00
100.00
Spring
.1,95
0.06
3.05
0.24
0.00
0.62
2.06
0.19
0.00
0.12
0.05
0.16
0.08
0.52
0.23
0.04
0.00
18.61
16.44
2.16
9.13
10.93
1.31
0.94
0.60
2.90
2.60
5.59
0.00
10.35
0.96
0.77
4.30
2.97
0.11
100.00
Summer
2.28
0.06
3.41
0.26
0.00
0.66
2.21
0.20
0.00
0.13
0.05
0.15
0.07
0.47
0.22
0.04
0.00
17.97
17.10
2.19
9.45
vjl.59.
1.32
0.92
0.56
3.00
2.81
5.26
0.00
9.59
0.89
0.71
4.00
2.34
0.09
100.00
Fall
3.56
0.07
4.64
0.33
0.00 >
0.80
2.68
0.23
0.00
0.15
0.05
0.12
0.06
0.35
0.17
0.04
0.00
15.70
18.71
2.22
10.19
13.43
1.30
0.84*
0.46 "
3.23
3.44
4.26
0.00
7.41
0.69
0.55
3.14
1.12
0.04
100.00
Percentage Contribution of Given SOL
to Ambient Sulfate Concentration Ir
Winter
68.23
2.87
1.92
1.71
0.23
7.41
6.77
1.56
0.76
0.76
0.15
0.18
0.11
0.31
0.13
0.03
0.00
0.00
3. SO
0.00
0.00
1.09
0.00
0.26
0.09
0.00
0.95
0.24
0.23
0.08
0.02
0.02
0.09
0.00
0.00
100.00
Spring
4.75
0.82
4.59
0.36
0.24
2.12
1.94
0.57
0.28
0.28
0.11
0.44
0.26
1.38
0.42
0.09
0.10
0.00
30.39
0.00
0.00
6.33
0.00
1.37
0.89
0.00
4.04
10.19
14.62
4.40
1.08
0.88
4.49
2.46
0.12
100.00
Summer
5.54
0.88
5.12
0.39
0.26
2.28
2.08
0.60
0.29
0.29
0.12
0.42
0.24
1.25
0.39
0.09
0.09
0.00
31.49
0.00
0.00
6.68
0.00
1.33
0.84
0.00
4.35
9.55
13.36
4.06
1.00
0.81
4.16
1.93
0.09
100.00
Fall
8.48
1.05
6.82
0.49
0.31
2.70
2.46
0.68
0.33
0.33
0.12
0.34
0.20
0.90
0.30
0.09
0.06
0.00
33.72
0.00
0.00
7.58
0.00
1.20
0.67
0.00
5.23
7.57
9.79
3.07
0.75
0.61
3.20
0.90
0.04
100.00
X Chang*
-11.71 -56.31 -56.15 -55.21
-------
measured on the south rim of the Grand Canyon. The Committee on Haze in National
Parks and Wilderness Areas of the National Research Council (1990) reviewed the
WHITEX report and concluded that its quantitative estimates were uncertain but agreed
qualitatively that NGS was a significant contributor to Grand Canyon sulfate at times in the
winter.
The operators of NGS sponsored an independent study conducted in the winter of 1990
(Richards et al., 1991) that concluded that NGS contributed only on average 9 percent (66%
maximum) of the sulfate on the south rim of the Grand Canyon during the January through
March, 1990 period. They found that the NGS plume was absent from Hopi Point on the
south rim of the Grand Canyon 67 percent of the time in January - March 1990. The
seasonal average visual range improvement predicted to result from 100 percent control of
NGS SO2 emissions was 2.5 percent.
Latimer (1990a,b; 1991) estimated the average improvement resulting from 100 percent
control of NGS emissions not only during the WHITEX measurements but also for the
entire winter season and for the other seasons. One set of calculations was performed only
for the WHITEX measurement period. Another set was done based on regional haze
model calculations using 1981 meteorology. The last was done based on haze puff model
calculations using five years of data (1986-1990).
90
-------
The average visual range improvements, assuming 100 percent control of NGS SO2
emissions, are summarized as follows:
WHITEX Episodes in 1987 (14 days) 13 - 19%
Winter Average based on 1981 Modeling 0.4 - 7.0%
2.9% (best estimate)
Winter Average based on 1986-90 Modeling 9% (original calculation)
2% (Richards and Prouty, 1991)
Modeling for Other Seasons based on 1981 Modeling
Spring 0.2-1.0% 0.5% best
Summer 1.0-2.4% 1.6% best
Fall 0.3-1.2% 0.7% best
Modeling for Other Seasons based on 1986-90 Modeling
Spring 0.6%
Summer 1.6%
Fall 4.4%
Thus, Latimer's calculations of winter average visual range improvements are in the range
of 0.4 to 9 percent, with a best estimate of approximately 3 percent. This compares quite
well to Richards et al. (1991) estimate of 2.5%, but is much lower than the improvement
of 13 to 19 percent calculated for worst-case (WHITEX-type) episodes. Richards and
Prouty (1991) made modifications to the HAZEPUFF model calculations of Latimer (1991)
to account for alternate assumptions regarding sulfate formation rates and sulfate light
scattering efficiency. The corrected value of 2 percent visibility improvement (rather than
the original 9%) may underestimate the sulfate formation rate at high humidities and the
effect of hysteresis on sulfate light scattering. Thus, the corrected value may be an
underestimate; however, it is likely that the original calculations are probably overestimates.
Thus, it is this author's opinion that 3 to 4 percent is the best current estimate of the winter
average visibility improvement on the south rim of the Grand Canyon that will result from
100 percent control of NGS SO2 emissions.
Latimer's calculations of improvements in other seasons are approximately 0.5 percent for
spring, 1.6 percent for summer, and 0.7 to 4.4 percent in fall.
91
-------
8. VISIBILITY AND SULFATE TRENDS
AT GRAND CANYON
Trijonis and Yuan (1978) studied visibility trends for the period 1948 to 1972 throughout the
Southwest. Although they only had airport observations of visibility, not the refined
transmissometer measurements available currently and did not have visibility data for Grand
Canyon, they were able to document a significant decline in visibility over these 25 years.
For example, in Winslow, in northern Arizona southeast of Grand Canyon, visibility declined
by nearly 50 percent (see Figure 41). Apparently, a large portion of this visibility decline
was attributed to the increased copper production in the Southwest and consequent SO2
emissions. SO2 emissions reached their maximum in the late 1960s and early 1970s when
emissions from Arizona smelters alone totaled 6000 tons/day (Latimer et al., 1978).
During 1967-68, a strike caused the shutdown of copper smelters throughout the region,
resulting in an abrupt decrease in regional SO2 emissions. Trijonis and Yuan (1978) showed
that sulfate concentrations dropped significantly throughout the Southwest with a 60 percent
drop in the Grand Canyon (see Figure 42). This translated into a regional improvement in
visibility (see Figure 43). For example, just south of the Grand Canyon, in Prescott and
Winslow, Arizona, visibility improved by 15 and 8 percent, respectively. Even though there
were two places in the Southwest with small visibility reductions during the copper strike of
1967-68, the effect of the regional reduction in emissions was still evident. For example, in
Farmington, New Mexico, in the Four Corners area, Trijonis and Yuan (1978) reported a
3 percent decline in visibility. However, Latimer et al. (1978) showed when winds were
from southern Arizona, where the largest smelter SO2 emissions occur, visibility was
significantly improved (see Figure 44).
There have been significant reductions in copper smelter emissions since the early 1970s.
For example, in Arizona alone, copper smelter emissions dropped 50 percent, from 6000 to
3000 tons per day, over the period from 1973 to 1976 (Latimer et al., 1978). By 1981,
Arizona copper smelter emissions had dropped to approximately 2400 tons per day and by
1987 they had dropped to about 600 tons per day (see Table 6). Thus, over the period from
the early 1970s to the early 1990s, Arizona copper smelter SO2 emissions have been reduced
by 90 percent.
92
-------
U)
•• Yearly Values
-• Three-Year Moving Averages
90
80
70
60
50
> 30
20
10
80th Percentile (Estimated)
90th Percentile
I I I f | II \ I
1948 1950 1955 1960
Year
.1 I I I
1965 1970 1972
Figure 41. Long-term visibility trends in Winslow, Arizona, 1948 - 1972. Source: Trijonis
and Yuan (1978).
-------
See Figure 2 for
names of locations.
o
Urban Site
Nonurban Site
250 tons/day SO
100
I
SCALE (miles)
200
Figure 42. Seasonally adjusted changes (in percent) in sulfate concentrations in the
Southwest during the copper strike of 1967-8. Locations of copper smelter emission sources
is shown by dots. Source: Trijonis and Yuan (1978).
-------
See Figure 1 for
names of locations.
o
Urban Airports
Nonurban Airports
250 tons/day S02
100
1
SCALETmiTes)
200
_J
Figure 43. Seasonally adjusted changes (in percent) in visual range in the Southwest during
the copper strike of 1967-8. Locations of copper smelter emission sources is shown by dots.
Source: Trijonis and Yuan (1978).
-------
100
u
-C
O r—
•i- CVJ
+•> I—
ID
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O) "O
to O>
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O U
X
C7) O)
•r- CT)
•— c
>, TJ
(O o:
a
<<- *«
o ^
-------
Sisler and Malm (1990) have shown a strong, direct relationship between copper smelter
emissions and summer sulfate concentrations measured at Hopi Point, on the south rim of
the Grand Canyon (see Figure 45). Cahill (1991) has shown that sulfate concentrations in
summer have decreased dramatically during the 1980s during the period when copper
smelter emissions were cut; however, during the more sta.gnant winter, when transport from
smelters would be less frequent, sulfate concentrations have increased (see Figure 46).
The National Park Service started measuring visibility routinely in the Grand Canyon in
1978. Teleradiometers were used until 1987, when the more accurate transmissometers were
substituted for the teleradiometers. Figure 47 shows the trends in average visual range for
each of three four-year periods: 1979-82, 1983-86, and 1987-90. There has been a
significant increase in visual range in the Grand Canyon between the early 1980s to the
middle and late 1980s in all seasons except winter. From 1979-82 to 1983-86, visual range
increased by 31, 16, 11, and 1 percent, respectively, for spring, summer, fall, and winter
seasons, respectively. Comparing visibility in 1979-82 with 1987-90 is quite tenuous since
two different measurement techniques were used (teleradiometers and transmissometers).
However, from 1979-82 to 1987-90, visual range increased by 13, 13, and 15 percent in
spring, summer, and fall, respectively, while winter visibility decreased by 12 percent.
These seasonal trends are noteworthy for two reasons. First, they suggest that long-range
transport of smelter SO2 emissions caused a significant fraction of Grand Canyon haze in
the early 1980s, which has since been dramatically reduced. Second, they suggest that such
long-range transport does not occur in the winter, when sources closer to the Grand Canyon
might be more significant contributors of sulfate.
97
-------
1000
900-
800 -
700 -
600 -
S 500 -
400 ~
300 -
200 -
100 -
0
00
* - HOPI POINT
0-CHIRICAHUA
300 600 900 1200 1500 1800 2100 2400
SMELTER S02 EMISSIONS
Figure 45. Relationship between copper smelter emissions and sulfate measured in
Chiricahua National Monument and Hopi Point, Grand Canyon National Park. Source:
Sisler and Malm (1990).
98
-------
Q)
73
1D -
II
|1 05
e
0.0
Hopl Point: January-February
90 81 52 83 84 95 36 87
year
89 90 91
2.0
0.0
Hopl Point: a summer
• winter
• i i i i i i i \ i i i i
79 80 81 82836485638768899091
year
Figure 46. Trends in sulfate concentrations measured at Hopi Point, Grand Canyon, 1980
1991. Source: Cahill (1991).
99
-------
Trends in Grand Canyon Visual Range
Comparing 3 4-yr Periods, Seasonally
30CH
V 250H
D)
£ 20CH
"co
.i 150H
'•g 100H
0
o
| 50H
6
m
Spring
Summer Fall
Season
Winter
1979-1982
1983-1986 M 1987-1990
Figure 47. Trends in visual range measured at the Grand Canyon, over three four-year
periods, 1979 - 1990.
100
-------
9. GOALS AND STRATEGIES FOR REDUCING HAZE
IN THE GRAND CANYON
The previous chapters have provided technical background on the composition and sources
of haze in the Grand Canyon. We have shown that past reductions in regional SO2
emissions have improved sulfate air quality and visibility in the Grand Canyon. It is clear
that, if the political will is there, future improvements could be made, for example, by
controlling San Joaquin Valley SO2 emissions (e.g., by switching from oil to natural gas
combustion there), by further controlling smelter emissions (most notably San Manuel and
Cananea), by controlling power plants (e.g., Navajo, Mohave, Huntington Canyon, and Jim
Bridger, or further controls on Four Corners), and by reducing motor vehicle emissions.
Figure 48 shows a set of possible goals for the Grand Canyon. The estimated annual
average light extinction coefficient for the Grand Canyon of 21 Mm"1 is shown in 1990. By
comparison, the estimated natural light extinction coefficient (due to Rayleigh scatter and
natural organics and other particles) is 15 Mm"1. Three possible goals are shown: reducing
anthropogenic extinction by 0.5, 1, and 2 percent per year.
Figure 49 outlines a strategy for meeting such visibility goals. It's technically feasible: is it
politically feasible?
101
-------
Alternate Long-term Goals
to Further Control Grand Canyon Haze
2100
1 %/yr
0.5 %/yr
2 %/yr
Natural Bext
Figure 48. Alternative long-term goals for improving visibility in the Grand Canyon. The
graph shows the target light extinction in the Grand Canyon, moving from the current 22
Mm'1 to the estimated natural condition of 15 Mm'1. Three alternatives are shown,
removing 0.5, 1, and 2 percent of the anthropogenic extinction per year.
102
-------
Step 1
Goal
definition
Technical Input
Step 2
(see
Figure
2b)
Step 3
(see
Figure
2c)
Step 4
(see
Figure
2d)
Step 5
Step 6
Selection
of
visibility
parameters
and (moving)
targets
Determination
need for
visibility-
specific
regulation
of
Development
of visibility-
specific
control program
Measurement
progress
toward
(moving)
visibility
target
of
Refinement of
plans
as needed
Selection of quantitative measures
Selection of visibility targets
Visibility monitoring
Emissions inventories
Emissions projections
Visibility modeling
Source attribution
Cost-effectiveness studies
Visibility monitoring
(a) Overview of the six basic steps
Figure 49. One suggested strategy for implementing a visibility protection and improvement
goal for the Grand Canyon. Source: Latimer (1990).
103
-------
Emissions of Interest
S02, NOX, HC, PM25, NH
Atmospheric Species
of Interest
N0? gas; sulfate, nitrate, and organic aerosol;
otner PM-2.5, PM-10
Research into the Effects
of Visibility Impairment
Atmospheric Chemistry and Physics
Psychophysics
Perception
Economic Benefits
Definition of Visibility Goal(s):
Reasonable progress (e.g., a 1%
reduction in impairment from previous
year's) toward the nation's vis-
ibility goal of preventing any
humanly perceptible visibility
impairment in Class I areas.
Related Air Quality
Issues
Acid deposition
Public health effects
Material damage
Vegetation and ecosystem damage
I
J_
Establishment of Visibility
Targets for -acn
Region and Year
Visual range or ext-nction
coefficient or surrogate
measure.
Go to 2(c)
(b) Determining visibility goals and targets.
Figure 49 (continued).
104
-------
Monitoring
data
Growth Factors:
Population •
Industrial
Commercial
Is current
visibility acceptable
(i.e., meeting target?)
Yes
Control Factors:
Effect of existing
and future
regulations
Future
emissions
projections
No
Visibility
modeling
Will emissions increase
in the vicinity of
Class I areas?
Yes
Will projected visibility
impacts be greater
than allowed by
target for given year?
No
Will emissions remain
relatively constant?
Yes
I
No
Yes
Proceed with strategy
development and
implementation
necessary to control
existing,or to prevent
potential future,
visibility impairment
Concerns about
long-term trends
may suggest a
need for later
reevaluation
[NO
Decreasing
emissions may
suggest that
visibility-specific
controls are
not needed
Go to 2(d)
(c) Determining whether visibility-specific regulation is needed,
Figure 49 (continued).
105
-------
Trend analysis
Dispersion and
receptor modeling
Source attribution
Which emissions
sources need to be
controlled?
Existing Sources Only
Retrofit emissions
control
Process modification
Fuel substitution
Both Existing and Future
Sources in Combination
Regional emissions
bubble or
offsets
Interstate and
international
coordination
Future Sources Only
NSPS for SOX &
NO,
Siting policy
Motor vehicle
standards
Urban and transpor-
tation planning
Which emissions
should be
controlled?
Coordination with
other air quality
objectives and
other emissions control
programs
SOj
Smelters
2
s
lants
ustry
H^i
Motor vehicles
Power plants
Oil industry
HC
Motor vehicles
Oil industry
1 1
Primary
Particulars
Point, area,
fugitive and
natural sources
Design of a coordinated,
cost-effective strategy
to control existing sources
and/or to manage future
emissions growth
(d) Designing and implementing the visibility-specific program.
Figure 49 (concluded).
106
-------
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of sulfur concentrations at Grand Canyon National Park. Atmos. Environ. 19:1263-
1270.
Appel, B. R.; Tokiwa, Y.; Hsu, J.; Kothny, E. L.; Hahn, E. (1985) Visibility as related to
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107
-------
Gebhart, K. A., R. A. Ahlbrandt, W. C. Malm, H. K. Iyer. (1988) Estimating the fractional
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Iyer, H. K., W. C. Malm, and R. A. Ahlbrandt. (1986) A mass balance method for
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John, W. (1986) A new method for nitric acid and nitrate aerosol measurement using the
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181.
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the prediction of anthropogenic visibility impairment. Volumes I, II, HI. U. S.
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Latimer, D. A.; Ireson, R. G. (1980) Workbook for estimating visibility impairment. U. S.
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108
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Latimer, D. A.; Hogo, H.; Chinkin, L. R.; Dudik, M. G; Ireson, R. G.; Irpan, P; Jacobson,
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contributions. Systems Applications, Inc., San Rafael, CA. SAI document no.
SYSAPP/85-038. 28 February 1985.
Latimer, D. A.; Chinkin, L. R.; Dudik, M. C; Hogo, H.; Ireson, R. G. (1985b)
Uncertainties associated with modeling regional haze in the Southwest. American
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Latimer, D. A. and R. G. Ireson. (1988) Workbook for plume visual impact screening and
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Latimer, D. A. (1990) Investigation of potential nonlinearities in the relationship between
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1990.
Latimer, D. A. (1990) Estimation of changes in annual visual range in Grand Canyon
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Latimer, D. A. (1991) Haze impacts on the Golden Circle of national parks of sulfur
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109
-------
Malm, W. C. and K. A. Gebhart (1988) Optical characteristics of aerosols at three national
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Malm, W. C. (1989) Atmospheric haze: its sources and effects on visibility in rural areas
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110
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Ill
-------
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112
-------
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113
-------
APPENDIX A
SEASONAL SUMMARIES OF
VISIBILITY IN THE GRAND CANYON
MEASURED BY TRANSMISSOMETER
(SOURCE: AIR RESOURCE SPECIALISTS, INC., 1991)
-------
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Winter Season: December 1, 1986 - February 28, 1987
4-HOUR AVERAGE VARIATION OF bfixt /SVR (EXCLUDING WEATHER-AFFECTED DATA)
4-UU -
^ 35° -
I
~ 300 -
LJ
O
< 250 -
Of.
| 200 -
o 150 -
or
§ 100 -
£
00 50 -
1 UU —
^ 80 -
£S 60 -
x 40 -
K 20-
INSTRUMENTATION INSTALLED
12/19/86
7 :
1
10 20 31
DECEMBER
li/irti
WlWllflnJlililll
|l fyf'fliffl
1 Ijnl'
HOURLY b . /SVR
c
Z
Q^ 250 ~
§ 200 -
to
> mn -.
a
-------
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Spring Season: March 1, 1987 - May 31, 1987
4-HOUR AVERAGE VARIATION OF bgxt /SVR (EXCLUDING WEATHER-AFFECTED DATA)
E
.*
350 -
300 -
o
< 250
Q:
o
o:
2
200 -
150 -
100
50
INSTRUMENT MALFUNCTION; REMOVED FOR SERVICING
\
.
- .uus
Oil
- .U 1 1
— n 1 o
.ui z
_ n i c *"N
1
— m Q _x
.ui y ^
X
no* °
~ .U^O j^
— ft"*B
~ .uoo
_ rtTQ
1 10 20 31
MARCH
^ 80 -
£, 60 -
i 40 -
K 20 -
1
||
Wl 1 MJ
M
HOURLY b, /SVR FREQUENCY OF
400
| 350 -
£ 300 -
z
K 250 -
5 200 -
to
> 150 -
1 100-
Q
JNSU
-FIG!
EWT-
10 20 30 10 20 31
APRIL MAY
II. Jlij ,
illifl 11 ii i iiWi jl i i
111 k n jilrfiii illi/iiMN' lii*)liii MM
"^nlAKu A/^M^n MiW ly|y\/v'''\'i/»'''i 'V» '*VJ1T
rWt^ V »V '
Excluding Including
Weather [0] Weather fx]
OCCURRENCE % b. . SVR b. , SVR
nnn CXI CXI
DAT-AJ-
10
-.011 20
30
~ . 0 1 5 i o 0
P /O
-.019,£, 80
"B 90
ft
- .038 FOR A GIVEN 7. OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
- .078 THE CORRESPONDING SVR VALUE.
i i i i T i i
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (%)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2208
2208
30
28
%
100
100
1
1
93
DATE PREPARED: 8/ 7/91
-------
400
350
j"
~ 300 -
ut
o
< 250
oc.
| 200
o 150
o:
5 100
to
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Summer Season: June 1, 1987 - August 31, 1987
4-HOUR AVERAGE VARIATION OF bext /SVR (EXCLUDING WEATHER-AFFECTED DATA)
50 -
....I...
INSTRUMENTATION REINSTALLED
7/7/87J
—r~
10
JUNE
20~
r—
20
JULY
"To"
20~
AUGUST
.009
- .011
- .012
100
80 -
60 -
40 -
20 -
HOURLY b , /SVR FREQUENCY OF OCCURRENCE
* 6X1 ^^^^
Excluding Including
Weather [0] Weather [X]
o
<
to
o
<
7Kfl _
Tflf\ _
o^n -
9nn -
1 ^r\ -
1 fipi —
Kf) «
c
i i ] t
A i t !
> * 1 \
•; ] !
5
! '
1
S "
. '
\
}
-011
- m o
- n^ <^*~
.Ul 0 I
— 010.*
- .020 «>
J3
A7D
^ .UJO
— r»7R
Jext
SVR
Jext
SVR
10
20
30
40
50
60
70
£10
90
.028
.024
.022
.020
.019
.017
.016
.014
.013
136
158
172
189
199
221
235
267
286
.039
.028
.024
.022
.020
.018
.016
.015
.013
98
136
158
172
189
209
235
250
286
FOR A GIVEN 7. OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (%)
- .019
- .025
- .038
- .078
X
«
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2208
2138
874
704
%
100
97
40
32
81
DATE PREPARED: 8/ 6/91
-------
400
~ 350
E
~ 300
LJ
u
< 250
oc
| 200
to
a 150
ce
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Fall Season: September 1, 1987 - November 30, 1987
4-HOUR AVERAGE VARIATION OF bext /SVR (EXCLUDING WEATHER-AFFECTED DATA)
t/5
100 -
50 -
.009
- .011
- .012
- .015
i
- .019
.025
- .038
- .078
10 20
SEPTEMBER
30
10
20
OCTOBER
31
10 20
NOVEMBER
30
100
80 -
60 -
40 -
20 -
HOURLY bext /SVR FREQUENCY OF OCCURRENCE
«tuu -
s—\
p
LiJ -inn -
o -300
Z
_J
to
> 1 (^n —
I OU
o
? mn -
o
Z
/•
>
C
i »
i i
e
f
) )
i
, 9 !
K /
, 5
i
\ '
e
'•
9
- fi 1 0
~ .Ul £.
****
— m ^*~
" .Ul 0 |
— D1 Q ^
.u i y o
*m
x
X)
Excluding
Weather [0]
bext SVR
Including
Weather [X]
bext SVR
10
20
30
40
50
50
70
80
90
.038
.028
.024
.022
.020
.018
.016
.014
.013
101
136
158
172
189
209
235
267
286
.076
.039
.029
.024
.021
.019
.017
.015
.013
51
98
131
158
180
199
221
250
286
FOR A GIVEN 7. OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (%)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2184
2053
1989
1563
7.
100
94
91
72
79
DATE PREPARED: 7/31/91
-------
400
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Winter Season: December 1, 1987 - February 29, 1988
4-HOUR AVERAGE VARIATION OF bext /SVR (EXCLUDING WEATHER-AFFECTED DATA)
350 -
n 1—
10 20
DECEMBER
2V
JANUARY
31
—I r~
10 20
FEBRUARY
~29~
.009
- .011
- .012
- .015
I
- .019
- .025
- .038
- .078
X
tu
100
80 -
60 -
40 -
20 -
HOURLY b . /SVR FREQUENCY OF OCCURRENCE
I "XI
Excluding Including
Weather [0] Weather [X]
ext
SVR
ts>
a
cc
a
z
in
1 nn -
en -
(
S
(
.
t ,
C'
j >
X
. <
)
' !
(
) )
r
c
) >
i
(
) >
(
)
— f!1 1
- O1 0
.Ul i.
s-^
- T\\ C.*~
^ .Ul O I
- fi1 Q .^
.Ul o ci
*
J3
mp
- .UoB
Jext
SVR
10
20
30
40
50
60
70
BO
90
.022
.018
.016
.015
.014
.013
.012
.011
.010
172
209
235
250
267
286
309
335
367
>.65
.169
.031
.020
.017
.015
.013
.012
.011
<6
23
123
189
221
250
286
309
335
FOR A GIVEN 7. OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (%)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2184
2164
1342
844
7.
100
99
61
39
63
DATE PREPARED: 7/31/91
-------
400
^ 350
|
~ 300 -
LJ
< 250
§ 200
CO
o 150
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Spring Season: March 1, 1988 - May 31, 1988
4-HOUR AVERAGE VARIATION OF bfixt /SVR (EXCLUDING WEATHER-AFFECTED DATA)
GO
100 -
50 -
- .011
- .012
20~
MARCH
lo"
20~
APRIL
20
MAY
.009
100
HOURLY bext /SVR FREQUENCY OF OCCURRENCE
350 -
co
O
a:
o
z
in
100
50
-8 *
- .012
Excluding
Weather [0]
bext SVR
Including
Weather [X]
b SVR
.019 J,
- .025 5
D
D
3
D
3
D
D
D
D
.036
.030
.027
.024
.023
.021
.019
.016
.012
106
127
141
158
165
180
199
235
309
.055
.035
.030
.026
.024
.022
.020
.017
.013
70
106
127
146
158
172
189
221
286
- .078
FOR A GIVEN 7. OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (%)
I
h .019 J,
- .025
- .038
- .078
X
v
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2208
2206
2141
1796
7.
100
100
97
81
84
DATE PREPARED: 7/31/91
-------
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Summer Season: June 1, 1988 - August 31, 1988
4-HOUR AVERAGE VARIATION OF bext /SVR (EXCLUDING WEATHER-AFFECTED DATA)
*uu -
_ 350-
je
~ 300 -
LJ
O
< 250 -
a:
| 200 -
00
o 150 -
ce
1 100-
2
°° 50 -
-. 80 -
*S 60 -
i 40 -
K 20-
M MALFUNCTION
,M
I
jfl. ,A ^ Wi^I !
•\f^Y\ w i
10 20 30 10 20 31
JUNE JULY
II .Jh I l/lAll
Ijt r yijrfli I j I IriiflllU
»MiiJii ''T'fkJ'ik ikiM IXii^ k lifvlilll *
lA^t 7)W '' w '^^Wt^uiAjlif 'flF M '
10
.If] j
f
N\
VI^Af
/IJ-W.jilj U
flP'irWTt
- .uus
- .011
- .012
- .015^
I
- .019 J,
.*-•
- .025 J5
- .038
- .078
20 31
AUGUST
,
l| . lylf 1 M
II ilfn Hill
f) |M| || |
HOURLY b. . /SVR FREQUENCY OF OCCURRENCE %
.inn ex* r»ftrt
.1 350 -
g 300 -
z
_i
5 200 -
> 1 K,n -
10
. nil r-n
30
. m o Aft
" .U1Z ^rU
* 50
mt\'* fin
E 70
, . mo -^ Rft
a * - 90
a I _ noc S
Excluding
Weather [0]
bext SVR
.042
.038
.035
.033
.030
.028
.026
.024
.021
91
101
109
116
127
136
146
158
180
Including
Weather [X]
bext SVR
.047 82
.040 96
.036 106
.034 112
.031 123
.028 136
.026 146
.024 158
.022 172
o
I
00
FOR A GIVEN * OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (Z)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2208
2175
1328
1108
%
100
99
60
50
83
DATE PREPARED: 8/ 6/91
-------
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Fall Season: September 1, 1988 - November 30, 1988
400
^ 350
E
~ 300 -
UJ
o
< 250
a:
3 200 -
150 -
tn
>
Q
o;
i 100
CO
50 -
4-HOUR AVERAGE VARIATION OF bext /SVR (EXCLUDING WEATHER-AFFECTED DATA)
—I 1—
10 20
SEPTEMBER
- .011
- .012
~30~
"To"
20~
OCTOBER
10 20
NOVEMBER
.009
i
- .019
.025
.038
L .078
30
100
~ 80-
fS 60 -
x 40 -
01 20-
HOURLY bex{ /SVR FREQUENCY OF OCCURRENCE
o
a:
o
o:
in
150 -
5
t
I '
) ^
I $
1 *
•, i
} '
, 5
i '
6
8
- .012
- .019
i
E
jt
X
v
- .038
- .078
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (%)
Excluding
Weother [0]
bext SVR
Including
Weather [X]
Jext
SVR
10
20
30
40
50
60
70
30
90
.046
.037
.033
.029
.026
.024
.022
.020
.018
83
103
116
131
146
158
172
189
209
.054
.041
.035
.031
.027
.025
.022
.020
.018
71
93
109
123
141
152
172
189
209
FOR A GIVEN 7. OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2184
2178
2156
1878
%
100
100
99
86
87
DATE PREPARED: 7/31/91
-------
400
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Winter Season: December 1, 1988 - February 28, 1989
4-HOUR AVERAGE VARIATION OF bext /SVR (EXCLUDING WEATHER-AFFECTED DATA)
350 -
^^
E
~ 300
UJ
o
< 250
Of.
c/3
200 -
150 -
o
a:
1 100
in
50 -
—I 1—
10 20
DECEMBER
10 20
JANUARY
—i r~
10 20
FEBRUARY
.009
- .011
- .012
- .015
i
h .019 e
.025
- .038
- .078
100
80 -
60 -
40 -
20 -
HOURLY b i /SVR FREQUENCY OF OCCURRENCE
400 -i . $£—: . • . • ' r -009
Excluding Including
Weather [0] Weather [X]
Jext
SVR
Jext
SVR
I 350 -
o
a:
_j
o
cc.
o
to
100 -
f
\
1
1
3
;
f
r
0 2
> f ?
: ! !
t 1
0 30 40 5
) 5
0 6
? *
0 7
j 5
0 8
s fi
0 9
0
•- .019
10
20
30
40
50
60
70
80
90
.035
.031
.028
.026
.024
.023
.022
.020
.019
109
123
136
146
158
165
172
189
199
>.65
.054
.035
.030
.027
.025
.023
.021
.019
<6
71
109
127
141
152
165
180
199
- .078
FOR A GIVEN % OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
CUMULATIVE FREQUENCY (%)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER -AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2160
2148
2147
1577
7.
100
99
99
73
73
DATE PREPARED: 7/31/91
-------
400
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Spring Season: March 1, 1989 - May 31, 1989
4-HOUR AVERAGE VARIATION OF bext /SVR (EXCLUDING WEATHER-AFFECTED DATA)
009
350 -
100
80 -
HOURLY bext /SVR FREQUENCY OF OCCURRENCE
Excluding Including
Weother [0] Weother [X]
3ext
SVR
ext
SVR
O
a:
<
in
o
cr
o
I
Ten ^
Ofifi -
1 t\r\ _
1 OU *
1 fin -
c
5
J 5
(
| J
£
I *
j «
t
i <
«
e
i
8
»
f
_ m o
» n i Q .w
,U1 a ^,
*t
i- .025 «
10
20
30
40
50
60
70
80
90
.034
.030
.027
.025
.024
.022
.020
.018
.015
112
127
141
152
158
172
189
209
250
.040
.032
.028
.026
.024
.023
.020
.018
.015
96
119
136
146
158
165
189
209
250
FOR A GIVEN 7. OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (%)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2208
2199
2167
1998
%
100
100
98
90
92
DATE PREPARED: 7/31/91
-------
400
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Summer Season: June 1, 1989 - August 31, 1989
4-HOUR AVERAGE VARIATION OF bext /SVR (EXCLUDING WEATHER-AFFECTED DATA)
HOURLY bext /SVR FREQUENCY OF OCCURRENCE
Excluding Includin
Weother [0] Weather
uext
SVR
Jext
SVR
LJ
o
o
cr
o
<
CO
«HJU -
9sn -
1 5f\ -
1OU
1 nr\ _
1UU •
(
?.
»
. . . s
« s
, 6
J $
e
f
J
i
g
. mo
• .Ul I
- f)i •<*"
- A1 Q ^
.Ul S i
*••
mp
- .OJB
. ATD
• .u/o
10
20
30
40
50
60
70
80
90
.034
.029
.027
.025
.023
.021
.020
.018
.015
112
131
141
152
165
180
189
209
250
.037
.031
.028
.025
.023
.022
.020
.018
.015
103
123
136
152
165
172
189
209
250
FOR A GIVEN % OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (%)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2208
2178
2153
1971
X
100
99
98
89
92
DATE PREPARED: 7/31/91
-------
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Fall Season: September 1, 1989 - November 30, 1989
4-HOUR AVERAGE VARIATION OF bfixt /SVR (EXCLUDING WEATHER-AFFECTED DATA)
009
1 1
10 20
SEPTEMBER
30
T
20
OCTOBER
10 20
NOVEMBER
HOURLY b . /SVR FREQUENCY OF OCCURRENCE
Excluding Including
Weather [0] Weather [X]
p
uj inn
Jj OUU -
z
> 1 f\r\
-*" l ou -
0
fV
o
z
$
(
, s
x1 5
E
T
C
f
C
}
9
- m Q
- F\ TB
• UJO
Jext
SVR
'ext
SVR
X
u
10
20
30
40
50
60
70
80
90
.026
.023
.020
.018
.016
.015
.013
.012
.011
146
165
189
209
235
250
286
309
335
.029
.024
.021
.019
.017
.015
.014
.012
.011
131
158
180
199
221
250
267
309
335
FOR A GIVEN % OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (%)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2184
2181
1781
1631
%
100
100
82
75
92
DATE PREPARED: 7/31/91
-------
400
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Summer Season: June 1, 1990 - August 31, 1990
4-HOUR AVERAGE VARIATION OF bext /SVR (EXCLUDING WEATHER-AFFECTED DATA)
009
HOURLY b . /SVR FREQUENCY OF OCCURRENCE
* cxi ^^^^^^^^^^
Excluding
Weather [0]
b._t SVR
Including
Weather [X]
Jext
SVR
o
a
IX.
350 -
300 -
250 -
200 -
150 -
100 -
50 -
C
)
) 5
(
/
> i *
f -X
I (
C
'
I c
! ?
)
- .011
- .012
- .0157
- .019 <
- .025
j
- .038
- .078
10
20
30
40
50
60
70
80
90
.034
.029
.025
.023
.021
.020
.018
.016
.013
112
131
152
165
180
189
209
235
286
.041
.032
.028
.025
.023
.020
.019
.017
.014
93
119
136
152
165
189
199
221
267
FOR A GIVEN 7. OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (%)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2208
2208
1760
1443
%
100
100
80
65
82
DATE PREPARED: 8/ 7/91
-------
400
^ 350
T
~ 300
LU
o
< 250
oc.
c/2
X
tr
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Spring Season: March 1, 1990 - May 31, 1990'
4-HOUR AVERAGE VARIATION OF bext /SVR (EXCLUDING WEATHER-AFFECTED DATA)
200 -
150 -
§ 100
" 50 J
.009
- .011
- .012
- .019
- .025
- .038
- .078
10
20
MARCH
31
10
20
30
10
20
31
APRIL
MAY
100
80 -
60 -
40 -
20 -
0
HOURLY bext /SVR FREQUENCY OF OCCURRENCE
Excluding Including
Weather [0] Weather [X]
Jext
SVR
o
z
oc.
o
a:
CO
1 nn -
I
>
(
) >
{
) 5
f
) )
i
5 J
\ **
..... f
, 5
6
>
I
I
I
- m o
e\4 c*~
.Ul 0 |
- m o ^
.ui y o
^
- no c ?
-U/O *
Jext
SVR
10
20
30
40
50
60
70
80
90
.042
.033
.028
.025
.023
.021
.019
.017
.014
91
116
136
152
165
180
199
221
267
.065
.041
.032
.027
.025
.022
.020
.017
.014
60
93
119
141
152
172
189
221
267
FOR A GIVEN % OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (55)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2208
2196
2125
1686
7.
100
99
96
76
79
DATE PREPARED: 7/31/91
-------
400
~ 35°
E
~ 300
LJ
O
< 250
<
o 150
tr
1 100
2
W 50 -
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Winter Season: December 1, 1989 - February 28, 1990
4-HOUR AVERAGE VARIATION OF bext /SVR (EXCLUDING WEATHER-AFFECTED DATA)
—1 1—
10 20
DECEMBER
—I 1—
10 20
JANUARY
3V
—1 1—
10 20
FEBRUARY
Is"
.009
- .011
- .012
- .015^
I
" .019 J,
.025 J
- .038
- .078
100
ft?
x
o;
HOURLY bext /SVR FREQUENCY OF OCCURRENCE
Excluding
Weather [0]
Including
Weather x
Jext
SVR
'ext
SVR
*uu -
p
uj Tnn -
0 JUU
•z.
rS 9tir> -
< onr\ _
^ /uu
5> 1 <^n _
•^ 1 OU
O
rf inn -
a
< <;n _
£ 30 -
to
/
V
^
'
>
c
)
)
1
) 5
:
/
) >
(
\ .5
1
) i
f
\
(1
9
9
10
- m 1 ?n
30
- fi1 0 AD
~ 50
- ni *\*~ fin
E 70
- H1Q -^ RA
•x 90
^
. mo rno
SVR
A*7B TUC /
• .U/o I nt i
.025 152
01Q 1QQ
.017 221
ni R 9^^
.015 250
014 9fi7
.013 286
ft19 7flQ
.011 335
^r»|^/^M tf nc 1
S LESS THAN 0
**r»DDCCDriKirMki/'
^UKKt.jrUNUIN(.
.325 12
n^n 107
.022 172
nia OftQ
.016 235
ni<\ 9<\n
.014 267
ni o ^fto
.uiz juy
.011 335
ur Tiur TUT
R EQUAL TO
JCV/D WAI 1 IT
oVK VALUL.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (55)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2160
2154
1959
1571
7.
100
100
91
73
80
DATE PREPARED: 7/31/91
-------
400
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Transmissometer Data Summary
Fall Season: September 1, 1990 - November 30, 1990
4-HOUR AVERAGE VARIATION OF bext /SVR (EXCLUDING WEATHER-AFFECTED DATA)
009
T T
10 20
SEPTEMBER
30
10
31
OCTOBER
10 20
NOVEMBER
I
a:
HOURLY bext /SVR FREQUENCY OF OCCURRENCE
Excluding
Weather [0]
bext SVR
Including
Weather [X]
Jext
SVR
•frUU ""
p
LJ "ifln _
z
_i
a
5 1 no _
< 1 UU ~
rf Eft _
5 so
CO
)
, C
3 )
I
) >
i
s \
' '
) (
f
I 5
(
}
) '
f
1
}
—
- m o _*
,u i y ^
^«
- ri'tp
- .uoo
10
20
30
40
50
60
70
80
90
.025
.022
.019
.017
.016
.015
.014
.013
.012
152
172
199
221
235
250
267
286
309
.044
.028
.022
.019
.017
.016
.015
.013
.012
87
136
172
199
221
235
250
286
309
FOR A GIVEN % OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (%)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2184
2181
1859
1435
%
100
100
85
66
77
DATE PREPARED: 7/31/91
-------
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Tronsmissometer Data Summary
Winter Season: December 1, 1990 - February 28, 1991
4-HOUR AVERAGE VARIATION OF bext /SVR (EXCLUDING WEATHER-AFFECTED DATA)
009
in
-\ r
10 20
DECEMBER
31
T r
10 20
JANUARY
10 20
FEBRUARY
28
I
a:
HOURLY bext /SVR FREQUENCY OF OCCURRENCE
txciuamg including
Weather [0] Weather [X]
Jext
SVR
o
ac.
10
•HJU -
\r\r\
JUU -I
1 UU
f
H
C
[ ,
, C
; >
i 5
!
s C
? '
1 $
1
, (
I ]
f
> '
\
)
- .UU3
-011
n 1 o
- .ui /
- m Q j*
<«_
- .Ooo
'ext
SVR
10
20
30
40
50
60
70
80
90
.024
.021
.020
.019
.018
.017
.015
.014
.012
158
180
189
199
209
221
250
267
309
>.65
.026
.023
.020
.019
.018
.016
.015
.013
<6
146
165
189
199
209
235
250
286
FOR A GIVEN % OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (%)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2160
2157
1895
1524
7.
100
100
88
71
80
DATE PREPARED: 7/31/91
-------
400
350
300
250
GRAND CANYON NATIONAL PARK (SOUTH RIM), ARIZONA
Tronsmissometer Data Summary
Spring Season: March 1, 1991 - May 31, 1991
4-HOUR AVERAGE VARIATION OF bex{ /SVR (EXCLUDING WEATHER-AFFECTED DATA)
<
>
o
o:
150 -
100 -
50 -J
.009
- .011
- .012
- .015^"
i
- .019 J
- .025 *
- .038
- .078
10
20
MARCH
31
10
20
30
10
20
31
APRIL
MAY
fr!
X
cr
100
80 -
60 -
40 -
20 -
HOURLY b f /SVR FREQUENCY OF OCCURRENCE
t exi
Excluding Including
Weather [0] Weather [X]
Jext
SVR
SVR
O
a:
o
oc.
o
"*^n -
\r\r\ -
9^n -
9nn -
1 DU
1 nn .
cri _
o 5
X
! 5
, £
( '
, 5
i <
L C
I
I i
. . ..*
^ E
5
— f!1 1
- m o
- m <;*"
— m a .*
.025 »
J3
. mo
~ .UJO
_ mo
~ .u/o
10
20
30
40
50
60
70
80
90
.032
.028
.026
.024
.022
.020
.018
.016
.015
119
136
146
158
172
189
209
235
250
.050
.032
.028
.025
.023
.021
.019
.017
.015
77
119
136
152
165
180
199
221
250
FOR A GIVEN % OF THE TIME THE
SVR IS LESS THAN OR EQUAL TO
THE CORRESPONDING SVR VALUE.
10 20 30 40 50 60 70 80 90
CUMULATIVE FREQUENCY (%)
TRANSMISSOMETER DATA RECOVERY
TOTAL POSSIBLE HOURLY AVERAGES IN THE TIME PERIOD
TOTAL COLLECTED HOURLY AVERAGES IN THE TIME PERIOD
VALID HOURLY AVERAGES INCLUDING WEATHER-AFFECTED DATA
VALID HOURLY AVERAGES EXCLUDING WEATHER-AFFECTED DATA
PERCENT OF ALL VALID AVERAGES NOT AFFECTED BY WEATHER
NUM
2208
2206
1838
1525
%
100
100
83
69
83
DATE PREPARED: 7/31/91
-------
APPENDIX B
MEAN RESULTANT WINDS IN 1981
IN THE MIXED LAYER
ON THE BASIS OF LFM WIND FIELDS
(Source: Latimer et al., 1985b)
-------
',1,1.1./
x
January.
February.
-------
linn
March.
I""!1111!
0 5 10
•tttrt/ttc
m/
April.
-------
/7777/
May.
0 5 10
•eltct/ifc
June.
-------
7/1
July.
•et«rt/»ec
August.
-------
f
77 y /
7 / / / /
77 / / M
7 7 7 / M
y 7 / / i i M
/ / / i J I M
September.
0 5 10
•ettri/ttc
irnii i
-------
\\\
\\\ \
\\\\ I','
November.
\ \ i / / /
\ I ///
December.
------- |