AIR POLLUTION EMISSIONS, ATMOSPHERIC
PROCESSES AND EFFECTS ON VISIBILITY
Prepared by:
Rudolf B. Husar and Janja D. Husar
Center for Air Pollution Impact and Trend Analysis (CAPITA)
Washington University
St. Louis, MO 63130-4899
Lecture Notes for
Air And Ecosystem Management Training Course
Chicago, IL, February 5-7, 1996.
USDA FOREST SERVICE
310 WEST WISCONSIN AVENUE
MILWAUKEE, WI 53203
INTRODUCTION
EMISSIONS
3
3
Sulfur Emission Trends
Spatial Emission Patterns of SOx, NOx, and VOC
3
4
ATMOSPHERIC PROCESSES
7
Atmospheric Transport
Atmospheric Transformation and Removal Processes
Space and Time Relationships and Scales
Wet Deposition Pattern and Rates
Airborne Ozone Concentrations
Aerosol Mass Concentration Patterns
Pattern of Aerosol Types by Sources
Extinction Theory
Relationship Between Aerosol Concentrations and Bext
Role of Meteorological Parameters or relating visibility to extinction theory
Visibility Monitoring Methods
7
9
10
12
15
21
24
28
30
32
33
HISTORICAL AND CURRENT VISIBILITY
36
Trends of Haze at Selected Sites
Regional and Seasonal Pattern
Historical Relationship Between SOx Emission and Visibility
38
39
39
REFERENCES
44
2
Introduction
Forest health is influenced by the environmental conditions that include soil, physical
climate, as well as the chemical climate of the atmosphere. The chemical climate of the
eastern United States currently deviates substantially from the pre-industrial conditions
due to anthropogenic emissions of sulfur, nitrogen, organics, and other chemicals. It is
known that acute exposure to high sulfur oxide or ozone concentrations lead to
demonstrable damage to forests. However, the long-term effects of atmospheric
chemicals, i.e. the role of chemical climate on forest health is less well understood.
Over the past decade or two it has been suggested that some forest species in the US
and Europe are vulnerable to regionally dispersed (>1,000 km) atmospheric chemicals.
Ozone, sulfur oxides, nitrogen oxides, and trace metals have been implicated as
possible regional damaging agents to forests and human health. Sulfate aerosols that
interfere with solar radiation are also known contributors to regional scale visibility
degradation.
This document describes the atmospheric science that is judged to be relevant to forest
systems. The discussion begins with the summary of atmospheric emission pattern and
trends for sulfur and nitrogen oxides as well as volatile organics (VOC). The major
atmospheric processes including the transport winds, chemical transformations, and
removal processes are described. The atmospheric concentrations and deposition
pattern of sulfur and nitrogen compounds are also presented.
The second part of this presentation is devoted to atmospheric visibility. The relevant
properties of atmospheric aerosols including size distribution, chemical composition and
optical properties, including the relationship between the optical and physico-chemical
properties are reviewed and the most common measurement techniques are also
presented. Finally, the spatio-temporal pattern of visibility over the US is presented in
visibility maps and trend charts.
Emissions
The main source of anthropogenic sulfur, nitrogen and VOC emissions is the
combustion of coal, oil, natural gas, as well as the smelting of metals. The driving force
for fuel consumption is the demand for energy by the residential-commercial,
transportation, and industrial sectors of the economy. From the turn of the century to
the 1970s, US energy consumption has been characterized by a steady increase of
total consumption and shifts from one fuel to another (Figure 1). From 1850 to about
1880, wood was the primary energy source. By 1990, and during the first quarter of this
century, rising energy demand was matched by the increase of coal use. Accelerated oil
and gas consumption began in the late 1930s and 1940s, such that by 1950 the energy
supplied by oil exceeded that of coal and maintained its rise up to early 1970s. By
1960s, natural gas surpassed coal as an energy source.
Sulfur Emission Trends
The historical trends of sulfur emissions for US are depicted in Figure 2. It is evident
that US sulfur emissions were comparable to the 1990 value throughout this century
3
with some fluctuations. Sulfur emissions due to coal consumption contribute about two
thirds of sulfur emissions. In 1990, the total US sulfur emissions were about 12 million
tons (24 million tons of SO2). The new Clean Air Act regulations mandate the reduction
of SO2 emissions to 10 millions by year 2010.
Figure 1. Trend for US fossil fuel consumption since 1850: (a) consumption by fuel type;
(b) fraction of total energy by fuel type (Husar, 1986).
Spatial Emission Patterns of SOx, NOx, and VOC
Currently, extensive emission inventories exist for SOx, NOx, and VOC. The National
Acid Precipitation Network (NAPAP) in 1985 compiled detailed inventories for SO x, NOx,
and VOC (Irving, 1991). Plots of the total (point plus area sources) annual
anthropogenic SOx emission densities are shown in Figure 3 Highest emissions are
4
concentrated in the Ohio River Valley and the northeastern United States. There are
also significant sources in southern and central Ontario.
Figure 2. US total, coal, and oil sulfur emissions.
Figure 3. The seasonal 1985 NAPAP SO2 emission fields (Irving, 1991).
5
Figure 4. The seasonal 1985 NAPAP NO2 emission fields (Irving, 1991).
Figure 4 shows the corresponding annual emission distribution for NOx over North
America. NOx emission density is defined as the sum of the NO2 plus NO emission
densities, expressed as kg NO2 ha-1 yr-1. The NOx emission density tends to be more
uniformly spread over the United States than the SO2 emission density. Figure 5
shows the corresponding values for the anthropogenic emissions of volatile organic
carbon (VOC) compounds, expressed as kg CH4 ha-1 yr-1. No estimates of Mexico
emissions were included in the NAPAP inventory: therefore Mexico appears unshaded
in all plots.
Figure 5. The seasonal 1985 NAPAP VOC emission fields (Irving, 1991).
Seasonal values of emissions density were also calculated for NAPAP. However, the
seasonality was found to be less than 10%.
6
Atmospheric Processes
The relationship between pollutant emissions and receptor concentrations and
deposition patterns is determined by atmospheric processes: transport, chemical
transformations and removals. This section provides a brief review of these processes.
Atmospheric Transport
The transport of pollutants by the winds is determined by the flow field consisting of the
mean flow vector and a fluctuating turbulent perturbation. The mean air flow pattern for
July over North America is depicted in Figure 6. The three major airmass source
regions that influence the continent are the northern Pacific, Arctic, and the tropical
Atlantic. During the summer the eastern US is under the influence of the tropical
airmass, entering the continent through the Gulf of Mexico and returning to the northern
Atlantic. In the winter season, the Arctic air stream penetrates deep into the continent,
displacing the moist tropical air.
The three transport processes that influence the regional dispersion are shear, veer,
and eddy motion (Figure 7). The vertical gradient of wind speed (i.e. wind shear) is
responsible for lagging of low elevation pollutants behind those in the upper layers. The
directional veer with height causes lateral displacement of a vertically uniform puff. The
eddy motion is due to random vertical and horizontal fluctuations caused by thermal and
mechanical turbulence.
Both the transport speed and direction of an air parcel vary from day to day as
illustrated in Figure 8. The end points of the two day trajectories are plotted for a source
in the central US. It is safe to state that over a full year the air parcels are heading in
virtually all directions from a source, while the entire pack is drifting with the prevailing
winds as indicated by the arrow. It is clear, that for the two day transport trajectories the
spreading of emissions in all directions is faster than the drift by the mean winds. This
phenomenon blurs the meaning of the terms “upwind” and “downwind”.
Figure 6. Schematic air flow over North America in July. The three predominant airmass
source regions are also indicated.
7
Figure 7. Schematic of three main mechanisms of dispersion in the atmosphere: wind
shear, wind veer, and horizontal eddy diffusion.
Figure 8. Endpoints of two-day old trajectories from a source released in the central US.
Figure 9. Deposition pattern around a point source, for a pollutant having (a) two-day
atmospheric residence time (b) four-day residence time.
8
The randomness of puff dispersion results in an oval concentration and deposition
pattern around a point source as illustrated in Figure 9 for a four day atmospheric
residence time. The inner contour for each source shows the area over which 50% of
the deposition occurs, the outer contour is for 66% deposition. It is evident, that for
many purposes the assumption of circular deposition pattern is quite adequate.
Atmospheric Transformation and Removal Processes
The major complication associated with atmospheric processes arise from chemical
reactions that transform primary pollutants (emitted by the sources) to secondary
pollutants that are formed within the atmosphere. Ozone, sulfate aerosols, nitrates, are
examples of secondary pollutants.
The nature and the interaction of atmospheric processes is illustrated in Figure 10. It
shows the sulfur budget where the arrows represent both rates and processes. In
general, the chemical reaction and removal processes are divided into dry and wet. Dry
processes occur in the free atmosphere in the absence of clouds, fog, or precipitation.
Wet processes, on the other hand, occur sporadically when an airmass encounters
clouds, fog or precipitation.
Figure 10. The global sulfur budget of the atmosphere including anthropogenic emission
from coal and oil combustion.
In many instances, the driving force for the chemical reactions is provided by solar
radiation that causes photochemical reactions to take place. In the case of ozone
formation, the main precursors (primary emissions) are NOx and VOC. In
photochemical reactions, there are also numerous short-lived intermediate species,
such as the OH radical that participate in the reactions.
The consequence of chemical transformations is that the secondary air pollutants are
created only after a day or two of atmospheric transport. Consequently, the effects of
these species are removed 500-1000 km from the source. Thus, regional haze
9
(visibility degradation), adverse effects on aquatic and forest systems, and health
impairment and materials damage occur on regional rather than local scale.
However, it should be emphasized that both the concentration and deposition field
around the point source is highest at the source itself, and declines with distance. It can
be said, therefore, that in spite of long range transport of air pollutants, every source
impacts on itself most (Figure 9).
Space and Time Relationships and Scales
As briefly stated in the above description atmospheric pollutant concentration patterns
contain endless detail and complexity in space, time, size and chemical composition. In
order to characterize the pattern that is useful for effects assessment, it is necessary to
organize and structure the pollutant pattern analysis into dimensions: space, time, and
chemical composition.
Temporal pattern and scales. The time dimension of pollutants extends over at least six
different scales (microscale to secular), Figure 11. The secular time scale extends over
several decades or centuries. Given climatic and chemical stability of the atmosphere
the main causes of secular concentration trends are changes in anthropogenic
emissions. The yearly scale is imposed by seasonal variation of solar radiation. A
significant, unique feature of the temporal domain is the existence of periodicities.
Emissions, atmospheric dilution, as well as chemical/removal processes are influenced
by the seasonal cycle. The weekly periodicity is unique among the time scales in that it
is imposed exclusively by human-induced emission changes. The synoptic scale
covers the duration of synoptic meteorological events (3-5 days). Its role is primarily
reflected in dilution and chemical/removal processes. The daily cycle is again imposed
by solar radiation and it strongly influences the emissions, dilution, and
chemical/removal processes. Microscale defines variation of the order of an hour
caused by short-term atmospheric phenomena.
Space-time relationships. The spatial time scales of aerosol pattern are linked by the
atmospheric residence time of particles. Short residence times restrict the pollutant
transport to a short distance from a source, causing strong spatial and temporal
gradients. Longer residence times yield more uniform regional pattern caused by long
range transport. The relationship between spatial and temporal scales for coarse and
fine particles is illustrated in Figure 12.
10
Figure 11. Time scales for atmospheric pollutants.
Figure 12. Relationship of spatial and temporal scales for different pollutants.
The aerosol residence time itself is determined by the competing rates of chemical
transformations and removal rates. Secondary aerosol formation tends to be associated
with multi-day long range transport because of the time delay necessary for the
formation. For fine particles, 0.1-1.0 m, the main removal mechanism is wet removal,
while coarse particles above 10 m are deposited by sedimentation. Ultrafine particles,
below 0.1 m also coagulate to form larger particles. As a consequence of weak
removal rates, aerosols in 0.1-1.0 m size range reside in the atmosphere for longer
11
periods than either smaller or larger particles. If aerosols are lifted into the mid- or
upper-troposphere their residence time will increase to several weeks.
Urban areas have strong spatial emission gradients and also corresponding
concentration gradients, particularly in the winter under poor horizontal and vertical
transport conditions. In the summer most urban areas have similar concentrations to
their non-urban background (Figure 13).
Figure 13. Space-time relationship in urban areas.
In mountainous regions, the strong concentration gradients are caused by both
topography that limits transport as well as the prevalence of emissions in valley floors.
Strong wintertime inversions tend to amplify the valley-mountain top concentration
difference. Fog formation also accelerates the formation of aerosols in valleys (Figure
14).
Figure 14. Space-time relationship in mountainous areas.
Wet Deposition Pattern and Rates
The pattern of wet deposition for SO4, NO3 and NH4 is shown in Figure 15, 16, and 17.
The spatial pattern indicates that the sulfate depositions over eastern US exceed the
western region by an order of magnitude. Within the eastern US the industrial states
12
north of the Ohio River Valley, between Illinois and New York show the highest sulfate
deposition. Also, the deposition during the summer season over the eastern US
exceeds the winter values by about factor of two.
The nitrate deposition is also higher over the eastern US, compared to the West.
However, the spatial pattern also indicates moderately high nitrate deposition over the
agricultural Midwestern states, between Minnesota and Kansas, particularly during the
second calendar quarter. The seasonality of the nitrate pattern is much more uniform,
but the warm season still shows higher deposition rates.
Figure 15. Quarterly deposition rates for SO4, 1992 using data from the National Acid
Deposition Network (NADP).
13
Figure 16. Quarterly deposition rates for NO3, 1992 using data from the National Acid
Deposition Network (NADP).
Figure 17. Quarterly deposition rates for NH4, 1992 using data from the National Acid
Deposition Network (NADP).
14
The ammonia deposition pattern is distinctly different from sulfate, since the highest
deposition rates occur over the agricultural Midwestern states, between Kansas and
Minnesota. Evidently, the ammonia deposition over the industrial states is not
significant compared to the agricultural states. Another feature of the ammonia
deposition pattern is that the peak occurs in the second quarter, rather than during the
summer.
It should be noted that these deposition patterns represent quarterly averages that hide
the strong deposition variation from one precipitation event to another. Also, the
seasonal deposition pattern at a specific location will tend to vary from one year to
another by about 15%. Hence, depending on the environmental stress of concern, the
relevant deposition data may be either the cumulative long-term average or the shortterm peak deposition.
Airborne Ozone Concentrations
It is well established that high airborne ozone concentrations can damage forests.
Regrettably, national climatic maps for ozone are not available. One of the most difficult
problems pertaining ozone is the calculation of “representative” concentrations. The
concentration at a given location has a diurnal, synoptic scale (3-5 days), seasonal, and
long-term trends that result from the interacting NOx, hydrocarbon sources, physical and
chemical removal processes, and atmospheric transport. Nevertheless attempts have
been made to compile the ozone distribution pattern. The daytime ozone concentrations
for non-urban locations over the eastern US for August 1978-1981 are displayed in
Figure 18 (Vukovich et al., 1985). The daytime ozone concentrations exceeding 60 ppb
cover the area of the Ohio River Valley and the mid-Atlantic states, New Jersey-North
Carolina. It is worth noting however, that the concentrations are about 40 ppb in the
upper Midwest. Hence, the regional variability of the summer daytime ozone is only
about factor of two because of the natural background.
Singh et al. (1978) have proposed a semi-quantitative picture of the seasonal variations
of the ozone in the lower layers of the atmosphere (Figure 19) The natural ozone
pattern is indicated by the shaded area marked A. Superimposed on this natural
background is a man-induced perturbation marked with B and C. At remote sites the
natural ozone concentration reaches the maximum in the early spring. In the areas
influenced by man-made sources, a summer peak may arise. Here again it is to be
emphasized that on the average the man-made ozone is a mere perturbation over a
substantial natural background.
15
Figure 18. Mean diurnal maximum ozone concentration isopleths for August 1978-1981
(Vukovich et al., 1985).
Figure 19. Idealized ozone variations at remote locations (Singh et al., 1978).
Atmospheric Aerosols and Visibility
This section reviews the main properties of atmospheric aerosols, atmospheric optics
relevant to visibility, and summarizes the spatial-temporal pattern of visibility.
Atmospheric particles originate from variety of sources and posses a range of
morphological, chemical, physical, and thermodynamic properties. Examples include
combustion-generated particles such as diesel soot or fly ash, liquid phase cloud droplet
16
transformation products and photochemically produced particles such as those found in
urban haze, salt particles formed from sea spray, and soil-like particles from
resuspended dust. Some particles are liquid, some are solids; others contain solid core
surrounded by liquids, and some are chain agglomerates. Atmospheric particles
contain inorganic ions and elements, elemental carbon, organic compounds, and crustal
compounds. Some atmospheric particles are hygroscopic and contain particle-bound
water. The organic fraction is especially complex.
A complete description of the atmospheric particle would include an accounting of the
chemical composition, morphology, and size of each particle and the relative
abundance of each particle type a s a function of particle size. However, most often the
physical and chemical characteristics of particles are measured separately and the
complete information is seldom available.
Aerosol size distribution is one of the most important parameters in determining the
atmospheric lifetime, deposition, and optical properties of particles. As a result, the
environmental, health effects are strongly dependent on size distribution. Light
scattering as also strongly dependent on particle size, and thus particle size
distributions have a strong influence on atmospheric visibility and radiative balance (i.e.
climate).
Particle diameters span more than four orders of magnitude, from a few nanometers to
one hundred micrometers. Combustion-generated particles, such as those from power
generation, from automobiles, an in tobacco smoke, can be as small as 0.01 m and as
large as 1m. Particles produced in the atmosphere by photochemical processes
range in diameter from 0.05 to 2 m. Fly ash produced by coal combustion ranges from
0.1m to 50m. Wind-blown dust, pollens, plant fragments, and cement dusts are
generally above 2m in diameter.
An important feature of atmospheric aerosol size distribution is their multimodal nature.
Measurements over the past decades (Whitby et al, 1972, Whitby 1978) show that
atmospheric aerosols may be classified as fine or coarse particles. Figure 20 shows the
ranges and classification of particles. The sources, formation mechanisms, and the
chemical composition of these two aerosol modes are different. In general, the two
aerosol size modes have independent spatial and temporal patterns. Coarse, dust
particles tend to be more variable in space and time and can be suspended through
natural or human-induced activities. Fine particles are largely of secondary origin and
their spatial-temporal pattern is more regional. Notable exceptions are urban-industrial
hotspots and mountain valleys where primary submicron size smoke particles can
prevail.
17
Figure 20. Typical size distribution of atmospheric aerosols.
Aerosol Chemical Composition. The chemical composition of atmospheric aerosols also
influences the visibility and human health. It is also known that some chemical
components of fine mass, such as sulfates influence the visibility degradation more than
other fine particle components.
The aerosol chemical composition is also important for identifying source types based
on chemical “fingerprints” in the ambient aerosol. Since aerosols reside in the
atmosphere for days and weeks, there is a substantial amount of mixing that takes
place among the contributions of many sources. At any given “receptor” location and
time, the aerosol is a mixture of tens or hundreds of source contributions each having a
chemical signature for possible source type identification.
Fine particles are generally composed of sulfates, organics, nitrates, elemental carbon
(soot), as well as trace metals. Each major chemical species have sub-species such as
acidic and neutral sulfates, light and heavy organics, ammonium and sodium nitrates,
etc. Fine particles are primarily made up from liquid droplets, mainly solutions of
sulfates, nitrates and organic matter, mostly of anthropogenic origin. A certain fraction
of fine particles are chain agglomerates contributed by combustion-generation
processes. ( Figure 21).
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Figure 21. (a) Scanning electron micrograph of coarse atmospheric particles >2mm.
Note the irregular shape of the particles. (b) Transmission electron micrograph of
submicron particles. Most of the submicron aerosol volume is made up from
liquid droplets (which leave circular residues) and of chain agglomerates (Husar,
1974).
The chemical composition of coarse particles is dominated by the elements of the earth
crust, Si, Al, Fe, suspended from soil. Near roadways, coarse particles may be
contaminated by lead and other trace metals. At ocean shores, coarse particles may
consist of sea salt arising from breaking of waves. Both resuspended dust and sea salt
are primary particles, carrying the chemical signature of their sources. The coarse
particles are mainly composed of irregularly shaped crystalline materials (Figure 21).
The above discussions emphasizes the fact that atmospheric aerosols are composed
from a mixture of chemical species, each having a unique size distribution. A vivid
example for the species size distribution is shown in Figure 22.
Hygroscopic properties of aerosols. Particles in the fine particle size range are
generally hygroscopic . This means that water vapor is taken up and released by
aerosols, depending on relative humidity of the ambient air. In particular, near 100%
RH the particle size may grow two or three times its dry size at low relative humidity.
The RH dependence of some typical aerosol species is shown in Figure 23.
Atmospheric fine particles are composed of a mixture of these salts and organic
species. Consequently, there relative humidity dependence is similar to these
compounds.
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The hygroscopicity of aerosols is relevant to atmospheric visibility, particularly at high
humidities. For this reason, aerosol optical measurements have to include relative
humidity for all their readings. Alternatively, the relative humidity of the sampled air has
to be regulated to a fixed value, e.g. 60%. The relative humidity correction factor used
in visibility studies was derived from field and laboratory data.
Figure 22. Aerosol species concentration as a function of particle size.
Figure 23. Theoretical growth curves for solution droplets of sulfuric acid and other
inorganic salts of interest at 25oC.
20
Table 1. Relative humidity correction factor for Bext
Relative Humidity
30
40
50
60
70
75
80
85
*90
Bext/BextRH30%
1.00
1.05
1.11
1.17
1.23
1.41
1.64
1.94
3.60
Bext/BextRH60%
0.85
0.90
0.95
1.00
1.05
1.20
1.40
1.65
3.10
Aerosol Mass Concentration Patterns
Aerosols are ubiquitous in the atmosphere. In continental environments, aerosol
concentrations vary from a few g/m3 to several hundred g/m3 in polluted urban
atmospheres. The current understanding of the US national aerosol patterns arise from
the two non-urban, regional background monitoring networks; the Interagency
Monitoring of Protected Visual Environments (IMPROVE) and the Northeast States for
Coordinated Air Use Management (NESCAUM), and from the mainly urban network, the
Aerometric Information Retrieval System (AIRS). The IMPROVE/NESCAUM non-urban
network consists of 50 sites located mostly in national parks and wilderness areas, with
the monitoring sites located at higher elevations.
The quarterly aerosol spatial pattern for the entire US PM10 (particles <10m) mass
concentrations is shown in Figure 24 a,b,c,d. In the eastern US the highest PM10
concentrations are seen during the summer (Q3) in the Ohio River Valley and around
urban centers in Georgia and Alabama. In the West the highest PM10 concentrations
are seen in the mountain valleys in fall (Q4) and summer (Q3) months.
21
A
B
22
C
D
Figure 24a-d. Elevation corrected PM10 spatial pattern.
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Pattern of Aerosol Types by Sources
The national pattern of aerosol types can be determined from the measurement of
tracer element concentrations. The main aerosol types are sulfates, organics, soot, soil,
and sea salt.
In order to partition the aerosol mass into the aerosol types, aerosol equations were
developed. These equations estimated the aerosol types by scaling up tracer species
characteristic for specific source types. The tracers were any aerosol species which
was solely attributable to one aerosol type. Any scaling factors which could not be
determined from the assumed chemical composition of the aerosol types were found
through a mass balance approach where the measured fine mass to the sum of the
aerosol types. Table 2 presents the equation and assumption used in estimating the
aerosol types.
As shown in Figures 24 a, b, c, d, the fine mass concentrations in the East are generally
higher than in the west. In the East, the organic and sulfur aerosol types (sulfate, water
and NH4+) constitute the vast majority of the aerosol mass in the East, accounting for
nearly 80% of the fine mass. This fraction is smaller during the winter (Q1 and Q4) then
the summer (Q2 and Q3), and is maximum during the third quarter. Soot in the
Northeast accounts for more than 10% of the fine mass during the winter, but only about
5% during the summer. Soils are a negligible fraction of the fine mass in the Northeast,
but account for about 6% of the fine mass in the Southeast.
In the Southwest, the fine mass is dominated by three aerosol types; sulfate (including
cation and water), organics, and soil. The sulfate and soil exhibit a seasonal pattern.
During Q1 and Q2 the sulfate and soil each account for about 25% of the mass. But
during Q3 and Q4, the sulfate increases to about 30% while the soil fraction decreases
to approximately 20%. The mass fraction of the organics in the Southwest is largest for
the cold season where it is approximately 20% of the mass. This fraction decreases for
the warm season where organics account for only about 10% during quarter two. The
soot and salt each account for about 4% of the mass in all four quarters.
The Northwest region differs significantly form the rest of the West. The sulfate faction
is the smallest in the country accounting for about 15% of the mass. The major aerosol
type is organics which account for more than 40% of the mass during quarters 1, 3, and
4. The second quarter organic fraction is much smaller than the other seasons
accounting for less than 25% of the mass, but the largest sulfate and soil mass fractions
in this region are found during this quarter. The Northwestern region has a large
percentage of its mass unaccounted for by the aerosol equations, about 20% for all four
quarters then other regions of the US.
24
E
F
25
G
H
Figure 24e-h. The mass fraction of each fine aerosol type at every location for Quarters
1-4. The size of each pie chart is dependent on the fine mass.
26
Table 2. Equations and assumptions used to estimate the fine aerosol types in Figures
24.
Sulfate:
NH4+:
Water:
Sulfate = 3*S
-all sulfur, S, is in the form of sulfate, SO42A) West
NH4+ = 1.125*S
-all sulfur is ammonium sulfate
B) East
NH4+ = CNH4 * 1.125*S
-the NH4+ represents (NH4+)xHy
-CNH4 = constant determined from the fitting process
Water = Cwater * (Sulfate + NH4)
-Majority of water is associated with sulfate
-Cwater = constant determined from the fitting process
Soil:
Soil = 1.89*Al+2.14*Si+1.4*Ca+1.35*Fe+1.2*SoilK
-all elements are in their oxide forms
-these elements account for the majority of the soil
-SoilK is the non smoke potassium equal to 0.12*Si
-Fe is equally split between FeO and Fe2O3
Soot:
A) NPS Stations: Soot = Abs/10 m2/g
-Abs = absorption of aerosol sample
-all absorption is caused by soot
-the absorption efficiency is 10 m2/g
Sea Salt:
A) Sea Salt = 3.2*NA
-sodium constitutes 31% of sea salt
Organics:
Organic = Corg*(H - NH4/Const)
- Corg and Const are constants determined from the fitting process
-all hydrogen is associated only with sulfate and organics
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Extinction Theory
The effect of the intervening atmosphere on the visual properties of distant objects (e.g.
the horizon sky, a mountain) theoretically can be determined if the concentration and
characteristics of air molecules, aerosols, and nitrogen dioxide are known along the line
of sight (Figure 25). The general solution of the radiative transfer equation, i.e.
determining the effects of air pollution on visibility is quite complex and difficult.
However, approximations can be made that simplify the optical calculations.
Contrast and visual range. An initial object contrast (Co) can be defined as the ratio of
object brightness minus horizon brightness divided by horizon brightness. Assuming a
relatively uniform distribution of pollutants and horizontal viewing distance, the apparent
contrast of large objects decreases with increasing observer-object distance (Figure
26). As given by Middleton 1952
C = Co (BT/Bo) eBext*x
where
C = apparent contrast at observer distance
Co = initial contrast at object
BT/Bo = ratio of sky brightness at target object to that at observer
Bext = extinction coefficient
x = observer object distance or visual range, Vr
For a black object, the initial contrast is -1 and:
C= (-1) eBext*Vr
The threshold of contrast for large dark targets varies between 0.01-0.05. For standard
observers the threshold contrast of 0.02 has been used.
0.02 = -e-Bext*Vr
Vr = 3.92/ Bext
where Vr is visual range.
This is the standard Koschmieder formula for calculating visual range. The basic
assumptions and limitations are that the sky lightness at the observer is similar to the
sky brightness at the object observed, that the pollutants are homogeneously
distributed, the viewing distance is horizontal, that the object is large and black and that
there is a threshold contrast of 0.02.
The extinction coefficient represents a summation of air and aerosol scattering and
absorption as well as gaseous interferences to light transmission:
Bext = Bscat+Bap+ Bag+BRg
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Figure 25. (a) A schematic representation of atmospheric extinction, illustrating: (i)
transmitted, (ii) scattered, and (iii) absorbed light. (b) A schematic representation
of daytime visibility, illustrating: (i) residual light from target reaching observer,
(ii) light from target scattered out of observer’s line of sight, (iii) airlight from
intervening atmosphere, and (iv) airlight constituting horizon sky. (For simplicity,
“diffuse” illumination from sky and surface is not shown. The extinction of
transmitted light attenuates the “signal” from the target at the same time as the
scattering of airlight is increasing the background “noise.”
Figure 26. Effect of an atmosphere on the perceived brightness of target objects. The
apparent contrast between object and horizon sky decreases with increasing
distance from the target. This is true for both bright and dark objects (Charleson
et al., 1978).
The terms Bscat and Bap depict particle scattering and absorption, while BRg and Bag
depict scattering due to atmospheric gases. The units of extinction are inverse
distance, e.g. 1/mile. The most commonly used units are km -1 and 10-4m-1. As
extinction increases, visibility decreases.
29
The particle free atmosphere at sea level has an extinction coefficient of about 0.012
km-1 for “green” light (wavelength of 0.05m), limiting visibility to about 320 km
(assuming no curvature for the Earth).
Of all gaseous air pollutants, only nitrogen dioxide (NO2) posses a significant absorption
band in the visible part of the spectrum. Nitrogen dioxide is strongly blue absorbing and
can color plumes red, brown, or yellow. In rural areas with NO2<0.01ppm absorption by
NO2 is not important (Figure 27).
At low particle concentration Rayleigh (air) scatter dominates. At aerosol levels of a few
g/m3 Bscat> BRg. At this point, the visibility is controlled by particle scattering.
Relationship Between Aerosol Concentrations and Bext
Aerosols interfere with light by scattering and absorption. Light scattering itself is due to
diffraction, refraction, and phase shift as illustrated in Figure 28.
Figure 27. Rayleigh scattering by air bRg) is proportional to (wavelength)4. Reduced air
density at higher altitudes causes a reduction of bRg. The NO2 absorption band
peaks at 0.4 m but vanishes in the red portion of the spectrum (Husar et al.,
1979).
30
Figure 28. Light scattering by coarse particles (>2 m) is the combined effect of
diffraction and refraction. (a) Diffraction is an edge effect whereby the radiation is
bent to “fill in the shadow” behind the particle. (b) The speed of a wavefront
entering a particle with refractive index n > 1 (for water, n = 1.33) is reduced. This
leads to a reduction of the wavelength within the particle. Consequently a phase
shift develops between the wave within and outside the particle leading to positive
and negative interferences. (c) Refraction also produces the ‘lens effect.’ The
angular dispersion by bending of incoming rays increases with n. (d) For
absorbing media, the refracted wave intensity decays within the particle. When
the particle size is comparable to the wavelength of light (0.1 – 1 m), these
interactions (a-d) are complex and enhanced. For particles of this size and larger,
most of the light is scattered in the forward hemisphere, or away from the light
source.
Figure 29. Single Particle Scattering and Absorption. For a single particle of typical
composition the scattering per volume has a strong peak at particle diameter of
0.5 m (m = 1.5-0.051; wavelength :0.55 m). The absorption per aerosol volume
however is only weakly dependent on particle size. Thus the light extinction by
particles with diameter less than 0.1 m is primarily due to absorption (Charleson
et al., 1978). Scattering for such particles is very low. A black plume of soot from
an oil burner is a practical example.
The two components of light extinction that are dependent upon aerosols is the particle
scattering Bscat, and absorption, Bap. The most important parameters relating aerosol
concentrations to light extinction are the particle's size, refractive index, and shape. If
31
these properties are known, then the light scatting and absorption can be calculated.
The scattering and absorption efficiency per unit volume of a typical atmospheric
aerosol particle as a function of the particle size is presented in Figure 29.
The scattering per unit volume (or mass of aerosol) depends strongly on particle size as
shown in Figure 29. For white light (0.36-0.68 m) the most efficient scattering occurs
at about 0.5 m. Particles <0.1 and >1.0 m are poor scatterers. For this reason, the
0.1-1 m subrange is referred to as a light scattering size range. Those chemical
species, e.g. sulfates, that contribute significantly to the fine particle mass at 0.1-1.0 m
also contribute to light scattering. On the other hand, dust particles in the coarse aerosol
fraction (>2.5) or ultra-fine combustion aerosols (<0.1m) are not significant contributors
to atmospheric extinction because of their poor scattering efficiency.
As shown in Figure 20, the typical aerosol size distribution has the aerosol mass
distributed into a fine mode and coarse mode. The two modes are usually unrelated in
that they have different compositions, sources, lifetimes, and removal mechanisms.
The source of much of the fine mode aerosol is transformations of reactive gases into
secondary aerosols, such as the transformation of sulfur dioxide into ammonium sulfate.
Other sources include primary particle emission from combustion and industrial
processes. The coarse mode particles usually are derived from mechanical processes
such as grinding operations, and suspension of soils and dust from high wind speeds.
Aerosol types, such as sulfate, are hydroscopic and collect water from the atmosphere
causing them to grow in size. The growth of the particles is dependent upon the relative
humidity (Figure 23) and can grow them to be in the optimal size range for light
scattering (0.5 m).
The poor scattering efficiency of large particles causes the coarse aerosol mass to
contribute less to the total scattering than the fine aerosol mass. In the Eastern US, the
contribution of the scattering due to coarse particle is negligible. This is because, the
coarse mass is between 50 - 75% of the fine mass, and the high humidities in the East
cause the secondary aerosol types, such as sulfate, to be near the optimal diameter for
scattering. However, in the arid Western US, coarse mass can is about twice the fine
mass, and the scattering of the secondary aerosols is not as efficient. Thus, coarse
mass scattering can be significant a part of the total scattering in the West.
Role of Meteorological Parameters or relating visibility to extinction theory
The extinction coefficient calculated from the visual range is influenced by both haze
and natural obstructions to vision such as rain, fog, and snow. The role of these natural
obstructions can be eliminated by discarding the readings when these meteorological
phenomena occur. Figure 30a-d illustrates the extinction signal with and without the
natural visibility obstructions. Figure 30a and b show an extinction coefficient for
Raleigh/Durham airport for 1961 and 1992, respectively. These figures clearly indicate
high excursions of extinction coefficient during both summer and winter seasons.
Figures 30c and d illustrate the corresponding Bext values with the natural visibility
obstructions removed. The data clearly indicate that the high Bext values are
eliminated and that there has been a substantial increase in the extinction coefficient
between 1961 and 1992, particularly during the summer season.
32
Figure 30. Daily pattern of Bext for Raleigh/Durham, NC for 1961 and 1992. In a and b all
data are shown regardless of the weather conditions. In c and d the days with fog
and precipitation are eliminated.
Visibility Monitoring Methods
The following monitoring methods are used most frequently in visibility studies.
Visual Range:
The visual range is a subjective concept, being the maximum distance at which an
observer can discern the outline of an object. The obvious limitations in actually making
a judgment of visual range includes the observers’ visual acuity, the number,
configuration, and physical and optical properties of the visible targets. Observer’s
subjectivity imposes a random component on the observed signal. The lower contrast
of real targets compared to black objects, imposes a systematic underestimate of visual
range. In addition, visibility is reported in quantized units, depending on the availability
of visible targets. Thus, an observation of 10 miles means that the visual range is
greater than 10 miles The reported visual range is always an underestimate of the
actual visual range and the calculated extinction coefficients are always overestimates.
In spite of these shortcomings, airport visual range observation data have provided an
impressive insight into national scale visibility patterns and trends.
Airport observations of visual range have been made since 1919 and computer archived
since 1947 (NOAA, 1982). However, the human observers are planned to be replaced
with instrumental visibility monitors, which might improve the quality of data collected,
33
but on the other hand, will produce a break in the longest historical air quality related
database.
Photography:
Photography has been an integral part of most visibility programs. The National Park
Service (NPS) alone has archived over 100,000 color transparencies documenting daily
changes in scenic appearance. Photography is usually the only technique that can
provide information on the frequency, duration, intensity, and (occasionally) even the
source of elevated pollutants (i.e., those not in contact with the surface). Photographs
are also valuable for identifying unusual events that can effect the performance of
instruments (e.g. wild fires, dust storms, patchy fogs). Finally, photographs are the
single most important medium for communicating visibility conditions to the public and
policy makers.
Deciview :
Deciview is a measuring unit of extinction and not a specific measurement method.
Deciview is a recently defined visibility index which, like its more familiar acoustic
analog decibel, can be shown to be linear with humanly perceived changes under
commonly occurring conditions.
Deciview is a simple logarithmic transformation of the extinction coefficient. Deciview is
zero in the aerosol-free atmosphere, and increases with increasing extinction:
Deciview = log (Bext/BRg)
A one or two deciview increment in visibility is noticeable.
Telephotometers :
Telephotometers are based on principle of operation analogous to the human eye. The
most practical application of a telephotometer is a contrast measuring device,
comparing the brightness of color of an object to a background.
Transmissometers:
Transmissometers measure the path averaged extinction coefficient (Bext), i.e. monitor
the intensity of light of known measured initial intensity after it has traversed a known
distance. The ratio of these two intensities is the transmission, which is a natural
logarithm of the product of path averaged extinction coefficient and path length. Longer
path length transmissometers are used in the cleaner air (West), while shorter paths are
used in more polluted situations (East). The short-path transmissometers have a useful
range of measurement only up to a few kilometers visual range, and thus, have little
value for general visibility monitoring.
Integrating nephelometers:
Integrating nephelometers (Charleson, 1969) measures light scattering (B scat) over
nearly the entire range of angles from 0 to 180. Nephelometer measurements are
made in an enclosed cell through which sample air continually being drawn by a pump.
Nephelometers, if combined with a fine particle size-selective inlet can provide valuable
information on the fine particle component of the scattering and also closely related to
34
visual range. The nephelometer has been a popular method to monitor optical
properties of aerosols and relating them to particle concentrations and properties.
The polar nephelometer:
The polar nephelometer measures the light scattered from a any chosen angle. This
allows a direct measurement of scattering phase function (light energy scattered as a
function of solid angle), which is important for predicting the effects of aerosol on the
appearance of the scene. Integration of these measurements over all angles yield the
scattering coefficient. Unfortunately, polar nephelometers are not easily adapted to
routine monitoring and have not seen much use except in laboratory situations.
Forward and backscattering:
Forward and backscattering instruments have been evaluated and used on a limited
basis by several federal government agencies for airport visibility and offshore fog
monitoring purposes (NOAA, 1982). The simple design and good reliability of these
instruments have made them popular candidates for automated visibility monitoring for
transportation safety purposes. Since only a portion of the scattered light is measured
and absorption is completely unaccounted for, these instruments must be calibrated for
typical aerosol situations.
Data Filtering and Aggregation
For purposes of spatial-temporal trend analysis, the raw visibility observations were
summarized as quarterly averages of noontime light extinction coefficient. For each
month and station, three different extinction coefficients were calculated. The first set
included all visibility data regardless of weather and pollutant conditions (BX). The
second group (FX) is composed of extinction coefficients excluding precipitation and
fog. For the third group (RH) a relative humidity correction was performed to
compensate for water vapor effects. This latter parameter is closely related to the dry
fine particle aerosol mass concentration. The functional form of this correction factor is
given in Table 1.
Data Processing and Presentation
The specific parameter that is plotted for the haze maps is the 75th percentile. While
this is unconventional, it constitutes the safest approach in that it does not require any
extrapolation or other adjustments to the data. More conventional statistical measures,
e.g. the mean, can be estimated as follows: from previous research, e.g. Husar et al.
(1979), the extinction coefficient is roughly lognormally distributed with a typical
logarithmic standard deviation of 2.5. For such a distribution, the 50th percentile is 0.5
times the 75th percentile, and the mean is 0.76 times the 75th percentile. Thus, if one
wishes to convert the maps, the scales of the intervals must be multiplied by the
appropriate fractions. We recognize that even if the haze is lognormally distributed
everywhere, its log standard deviation will tend to vary geographically and seasonally.
The available data suggest, however, that it ranges between 1.6 and 3.4.
The spatial patterns are presented on contour maps. The contours were derived from
the station-point observations using a spatial extrapolation scheme. In the first step, the
data from the random locations were projected to a uniform grid (120x180) that covers
35
the conterminous U.S. The gridding used inverse distance squared (1/r2) as the station
weighing factor. The extrapolations outside the U.S. boundaries were clipped to
eliminate spurious extrapolations. The resulting contours are shown in Figure 31. The
shades are 0.03 km-1 apart. The darkest shade (black) has an extinction coefficient
(75th) percentile of >0.2 km-1 which corresponds to 1.9/0.2=9.5 km visual range. The
lowest contour is set at 0.05 km-1which corresponds to 1.9/0.05=38 km visual range.
Since these values represent the 75th percentile of Bext the median and the mean can
be estimated by assuming a logarithmic standard deviation, e.g. 2.5 for such a
distribution. The median of the contour range is between 18 and 76 km, while the mean
is between 12.5 and 50 km.
Figure 31. United States trend maps for the 75th percentile extinction coefficient, Bext for
winter (Q1), spring (Q2), summer (Q3), and fall (Q4). Bext [km-1] is derived from
visual range, VR, data by Bext = 1.9/VR. Data during natural obstructions to
vision (rain, snow, fog) were eliminated.
Historical and Current Visibility
The U.S. haze patterns and trends since 1960 are presented in 16 haze maps that
represent four time periods and four calendar quarters, seasons (Figure 31). The
selected time periods are 5 year averages centered at 1960, 1970, 1980, and 1990.
The discussion below will encompass the overall national pattern and trends. All these
data were obtained from human visual range observations.
36
The overall national view shows two large contiguous haze regions, one over the
eastern U.S. and another over the western Pacific states. The two haze regions are
divided by a low-haze territory between the Rocky Mountains and the Sierra-Cascade
mountain ranges. This general pattern is preserved over the past 30-year period.
However, notable trends have occurred over both the western and the eastern haze
regions. The haze trends over the visually pristine mountainous western U.S. can not
be evaluated here due to the poor distance resolution of the visual range database,
particularly at high visibilities. We recognize that this is unfortunate since much of the
recent interest in the optical environment arises from the possible significant
deterioration in the pristine southwestern states. The haze pattern and trends in this
region can only be assessed by using higher resolution dedicated aerosol/haze
networks.
The haziness in the western Pacific states covers all of the coastal states, California
exhibiting the highest values. In the 1960s a large fraction of western California had low
visibility particularly during Quarters 1 and 4. By the 1990s the visibility along the
Pacific Coast has improved markedly for all seasons.
The eastern haze region extends from the East Coast to the Rocky Mountains. The
western boundary of the eastern haze region is remarkably constant over both the
seasons and the years. In fact, the mid-section of the U.S. extending from the Rocky
Mountains to the Mississippi River changed little over the 30-year history.
The most dynamic pattern can be observed over the eastern U.S. extending from the
Mississippi River to the East Coast. There is both a significant seasonal variation over
the region and also a significant secular trend over the past 30 years. Furthermore,
these seasonal and secular (long-term) trends are different for sub-regions within the
eastern U.S., such as the Northeast, Mid-Atlantic and Gulf States regions.
In the 1960s the highest extinction values were recorded for the cold season, Quarters
1 and 4, with significantly lower values for the warm quarters (Q2, Q3). The remarkable
reduction in the cold season haziness and the strong increase during the warm season
has shifted the haze peak from winter to summer. Consequently, there was also a
regional shift in the highest haze pattern. In the 1960s the worst haziness occurred
during the cold season around Lake Erie and the New York-Washington megalopolis.
By the 1990s the worst haziness has drifted southward toward Tennessee and
Carolinas and it now occurs in the summer season.
The decade of the 1980s shows less change than the earlier decades. However, there
was a continued haze reduction in the Northeast, north of the Ohio and east of the
Mississippi Rivers. The southeastern U.S. as well as the Pacific states have remained
virtually unchanged in the 1980s. It is evident that the haze trend database provides a
way to monitor the effectiveness of the emission reductions from the 1990 Clean Air Act
Amendment. Previous work (Husar and Wilson, 1993) has linked the regional and
seasonal shifts in eastern U.S. haziness to haze precursor emission patterns, which
include sulfur and organics.
37
Trends of Haze at Selected Sites
This section illustrates the haze pattern at selected sites. The primary purpose of this
detailed examination is to illustrate the overall pattern of the daily haze signal and also
to illustrate some of the problems associated with the visual range data.
The daily extinction coefficient (excluding fog and precipitation) for Houston, TX and
New Orleans, LA is shown in Figure 32 a and b. The 32 year daily time series exhibits
a significant amount of noise but there is also evidence of long-term trends. For
example, visual examination reveals that in the 1970s the Bext was generally higher
than in the 1980s for both sites. The long-term time series also reveals systematic
problems due to the visibility threshold effect. For example, during 1970-1973 in
Houston, TX the lowest extinction coefficient was 0.16 km-1. This threshold is caused
by a change in the maximum visual range reported at that site. A similar threshold
problem is evident for New Orleans, LA during 1980-1988.
Figure 32. Daily variation of Bext for Houston, TX and New Orleans, LA. Note the change
in the visibility threshold over time.
Correction of these threshold anomalies is possible if over 25% of the data is above the
threshold. For the above two sites, this appeared to be the case. Consequently, the
75th percentile of the Bext was used for observing the overall pattern.
38
Regional and Seasonal Pattern
This section presents secular trends and seasonal pattern for six eastern U.S. regions,
as depicted in Figure 33. The trend graphs represent the average Bext (75th percentile)
for the stations located within the designated region. The trends are presented for
Quarters 1 and 3 separately. The northeastern U.S. exhibits an increase of Quarter 3
haze between 1960 and 1970, and a steady decline between 1973 (0.22) and 1992
(0.12). In the winter quarter the haziness has steadily declined from 0.15 to 0.10 in the
30-year period.
Figure 33. Secular haze trends (1960-1992) for 6 eastern U.S. regions.
Historical Relationship Between SOx Emission and Visibility
Because sulfates are currently the major contributor to light extinction in the East, it is of
interest to compare visibility trends in the East to sulfur oxide emission trends. The
comparison is separated by Northeast versus Southeast, and winter versus summer in
light of the strong regional and seasonal differences discussed previously.
The emission trends for the comparison base have been compiled by using historical
yearly emission data by Gschwandtner et al. (1985) and Husar (1986), as well as the
historical monthly emission data by Knudson (1985). Figures 34 a and b illustrate the
historical emission trends (by region and season) since 1960. The emissions are
expressed as million tons of sulfur/yr. The areas are 1.2 and 1.1 million km 2 for the
Northeast and Southeast, respectively. Hence, the emission units on the graphs can be
interpreted approximately as grams per square meter per year.
39
Figure 34. Sulfur emission trends for January (+) and July (O) for northeastern (top) and
southeastern (bottom) U.S.
The computerized visibility trend data cover only the period since 1948. For this
emission trend comparison, the visibility trends have been smoothed by using threeyear moving averages. It should be noted that the general trend patterns are not
sensitive to variations in defining “Northeast” versus “Southeast”.
The trends of haze and sulfur emissions for the Northeast in the winter (January) are
depicted in Figure 35a. The emissions show a peak in the 1940s, and in the early
1070s, followed by a significant decline into the 1980s. Overall, there is a general
decline during the 40-year period. The winter haziness also exhibits a general decline,
but shows more year to year fluctuations.
40
Figure 35. Comparison of sulfur emission trends (O) and extinction coefficient (+) for
northeastern US, during the winter month (top) and summer month (bottom).
The corresponding trends for the summer season (July) are illustrated in Figure 35a.
The summer emissions again show the two peaks, but the peak around 1970 is more
pronounced. Also, the decline since 1970 is not as significant as the decline of winter
emissions. The summer haziness in the Northeast shows the lowest values in the
1950s and 1960s with significant increase in the late 1960s. Since about 1970, there is
no significant decline except for 1983. Overall, the emission and trend haze show
certain correspondences, particularly in the 1950s and 1960ss, but they deviate
somewhat since 1970s.
The winter haze and emission trends for the Southeast are shown in Figure 35 a and b.
Winter emissions rise moderately until the early 1970s, when a slight decline begins.
The winter haziness also shows a moderate increase up to the early 1970s. The dip in
winter haziness around 1977 has no correspondence in the emission trends. The
comparison of summer sulfur emissions and haziness for the Southeast is found in
Figure 36b. There is a remarkable correspondence, increasing values from the late
1940s through the early 1970s followed by a leveling off since then.
41
Figure 36. Comparison of sulfur emission trends (O) and extinction coefficient (+) for
southeastern US, during the winter month (top) and summer month (bottom).
The statistical relationship between historical sulfur emissions and extinction coefficient
is further illustrated as a scatterplot in Figure 37. Here, yearly emissions and extinction
coefficient are aggregated over the entire East (states east of the Mississippi) In Figure
37, the correspondence between yearly sulfur emissions and yearly extinction
coefficient is fairly close. As noted previously, the relationship between extinction
coefficient and sulfur emission emissions depends on region and season. However,
one aspect that is the same for both regions and both seasons is that a 50% change in
sulfur emissions from current levels is statistically associated with about a 30% change
in light extinction.
42
Figure 37. Relationship between yearly extinction coefficient and yearly sulfur emission
for the entire East.
In conclusion, these data show that trends in the seasonal sulfur emissions provide a
plausible explanation for the observed seasonal trends of atmospheric extinction
coefficient over the eastern United States. However, such qualitative comparisons do
not provide conclusive evidence of a cause-effect relationship. Also, the pattern of haze
and sulfur emissions for the Northeast and Southeast tend to deviate at times. The
causes of such deviations may include variabilities due to meteorology as well as
potential errors in both emissions and visibility data. Finally, a one-to-one relationship
cannot be expected since the haziness in one region may be influenced somewhat by
emissions in the neighboring regions. A more detailed emission-haze trend analysis
could be conducted using a regional haze models that incorporates both the changes in
emissions as well as meteorological data for individual years. Both emissions and wind
field data are available for such retrospective studies.
43
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44