CHARACTERISTICS
OF
PARTICIPATE PATTERNS
1957-1966
U.S. DEPARTMENT OF HEAITH, EDUCATION, AND WELFARE
Public Health Service
Environmental Health Service
-------
CHARACTERISTICS
OF
PARTICULATE PATTERNS
1957-1966
by
Robert Spirtas
and
Howard J. Levin
Division of Air Quality and Emission Data
U.S. DEPARTMENT OF HEALTH, EDUCATION, AND WELFARE
Public Health Service
Environmental Health Service
National Air Pollution Control Administration
Raleigh, North Carolina
March 1970
For sale by the Superintendent of Documents, U.S. Government Printing (Mice
Washington, D.C., 20402 - Price 50 cents
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The AP series of reports is issued by the National Air Pollution Control Adminis-
tration to report the results of scientific and engineering studies, and information
of general interest in the field of air pollution. Information reported in this series
includes coverage of NAPCA intramural activities and of cooperative studies con-
ducted in conjunction with state and local agencies, research institutes, and in-
dustrial organizations. Copies of AP reports maybe obtained upon request, as
supplies permit, from the Office of Technical Information and Publications, Nation-
al Air Pollution Control Administration, U. S. Department of Health, Education,
and Welfare, 1033 Wade Avenue, Raleigh, North Carolina 27605.
National Air Pollution Control Administration Publication No. AP-61
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ABSTRACT
The National Air Surveillance Networks (NASN) have collected samples of
suspended particulate matter since 1957. The resulting suspended particulate
data are graphically summarized by the application of Whittaker-Renderson Type
A curve-smoothing formulas to 10 years of data. Data from 60 urban stations and
ZO nonurban stations were studied by this technique, which brings out the under-
lying cyclical patterns and long-term trends in nationwide levels of suspended
particulate matter. Seasonal patterns are evident for many urban and nonurban
stations, and the seasonal characteristics of the two types of stations contrast
sharply. Long-term trends are downward at many center-city urban sites, but
are upward at some nonurban sites.
-------
CONTENTS
INTRODUCTION . . . 1
DEVELOPMENT AND APPLICATION OF THE METHOD ... 3
DISCUSSION . . . ... 9
SUMMARY . . . . . . 17
TREND IN TOTAL SUSPENDED PARTICULATES
PART I. Urban Sites - . 19
PART II. Nonurban Sites . . 79
REFERENCES . . 101
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CHARACTERISTICS
OF PARTICULATE PATTERNS
1957-1966
INTRODUCTION
The National Air Surveillance Networks (NASN) have been collecting samples
of suspended particulate matter since 1957. Review and careful evaluation of the
substantial amount of data accumulated in the first 10 years of Network operation
is now appropriate. Previous analyses have generally been limited to comparisons
of means and variances, or to application of other conventional statistical tech-
niques. This publication presents a time-series approach to the study of the data;
it considers particulate levels as a. dynamic series of events occurring over a
10-year interval. This approach brings out some of the more subtle characteris-
tics of the data. There were more than 350 sites for which at least 1 year's data
had been collected during the period 1957 through 1966. From this resource, 60
urban and 20 nonurban sites were selected for analysis on an individual basis
because of their continuity of data over the 10-year period.
The data are graphically summarized by the application of Whittaker-Hender-
son Type A curve-smoothing formulas, because the derived curves provide a
visual perspective of seasonal patterns and long-term trends and offer insights
into the nationwide behavior of the concentrations of suspended particulate matter.
Other techniques are available for analyzing these data, but mathematical smooth-
ing summarizes masses of data most comprehensibly (Figure 1). In addition to
mathematical smoothing, standard statistical techniques have been used to cate-
gorize the 80 sampling sites with respect to seasonal patterns and long-term trends
Suspended particles are a. conspicuous class of air contaminant because they
are largely responsible for the most visible consequences of air pollution the
dirt, the grime, and the soiling of materials. The subtle health effects of parti-
culate matter, however, are more ominous. Some particles contain biologically
active substances, such as benzo-a-pyrene, which may be carcinogenic. ^ Parti-
cles also absorb and adsorb harmful gases such as sulfur dioxide and can carry
these gases deep into the lungs. Other particles may act as catalysts for chemi-
cal reactions in the atmosphere that produce harmful substances. Various systems
of the human body may be impaired by particles. The suspected impact of sus-
pended particles on health and environment necessitates an understanding of partic-
ulate levels and their trends.
-------
Objectives of the NASN include determination of the nature and extent of air
pollution and the study of trends in the levels of various atmospheric contaminants.
To accomplish these goals, a standardized sampling technique for measuring sus-
pended particles in the air was developed. Suspended particulate samples are
collected by a high-volume sampler, -which draws air through an 8- by 10-inch
glass-fiber filter. This instrument can extract 99. 9 percent of all particles 0.3
micron or greater in diameter. 2 Generally, air flows through the filters at an
average rate of 1. 4 to 1. 7 cubic meters per minute. The accumulated matter on
the filter retards the flow of air; consequently, flow rates are sometimes lower at
sites with high particulate concentrations.
Under the current NASN procedures, samplers are operated for 1 randomly
selected day during each Z-week sampling period. Although this procedure should
produce 26 valid samples each year at each site, equipment breakdowns, damaged
filters, or skipped sampling periods often limit the number of samples at a site.
Those filters that survive quality control intact undergo extensive laboratory anal-
yses, and the resulting data are statistically compiled.
The National Air Pollution Control Administration already has published many
of these data. The 1963 summary of NASN data on suspended particles discusses
a nationwide downward trend for the period 1957 through 1963 which becomes
apparent in a comparison of annual geometric mean concentrations of suspended
particles at center-city urban stations. A subsequent publication, "The Trend of
Suspended Particulates in Urban Air, 1957-1964, "^ primarily concerns the marked
decrease in suspended particulate levels at selected sites in 1961. It establishes
the statistical significance of the decrease and concisely summarizes possible
explanations for the short-term changes.
The present study encompasses the data collected over the 10-year period
from 1957 through 1966 for selected urban and nonurban sites. Urban sites are
located in the downtown, center-city area. All downtown stations, however, are
not equivalent. They are not uniformly situated, and they are characterized by
environmental peculiarities. Nonurban stations are positioned in supposedly rural
areas. Hence the nonurban category includes a wide diversity of site locations,
such as proximate, intermediate, and remote. ^ Because of the differences among
sampling sites, care should be used in drawing conclusions from comparisons of
such data. The inclusion of a. large number of sites in this study, however, per-
mits generalizations about typical urban and nonurban characteristics.
CHARACTERISTICS OF PARTICULATE PATTERNS
-------
DEVELOPMENT AND APPLICATION OF THE METHOD
The development of a technique for summarizing the 10 years of data accumu-
lated at each sampling site was an initial concern in this study. At first, raw data
values -were plotted on graphs, and the adjacent raw data points were connected
by straight lines. Wide fluctuations in the data, however, prevented any substan-
tive detection of site characteristics. A curve-smoothing technique was finally
employed to transform the irregular spiked graphs to curves which could be more
readily interpreted and which emphasized the patterns and trends in the raw data
graphs.
On the average, 250 samples have been collected at each of the 80 selected
sites. Since it had been determined that the resulting data can be best interpreted
if converted to a more comprehensible form such as smoothed curves, this con-
version required certain assumptions. This section explains briefly the methods
and assumptions used in converting the data.
At the outset, criteria for data subjected to analysis were necessary. For a
year's data to be adequate, a minimum number of samples must be collected in
several time periods, distributed evenly throughout the year. A year for which
adequate data are available is designated a "valid year"; others are referred to
as "invalid years."
The selection of sites included in the study was based upon a criterion of 7
or more valid years of NASN data from 1957 through 1966. Kent County, Delaware,
for which only 6 years of valid data are available, is the only deviation from the
criterion. This nonurban site -was included to provide a picture of nonurban partic-
ulate concentration in the proximity of the densely populated eastern seaboard. No
nonurban site had a valid year in 1957, because of the priority given to the estab-
lishment of urban sites in the reorganization and development of the current NASN.
This study provides in condensed, graphic form all the valid data for each of
80 sites from 1957 through 1966. Particulate levels have been plotted for each
sampling period, and graphs have been constructed by connecting the points con-
secutively (Figures 2-81). Two graphs have been prepared for each site. The top
graph in each figure presents the raw data curve and a superimposed smoothed
curve approximately representative of seasonal patterns. The lower graph depicts
the same raw data curve and a superimposed curve illustrating long-term trends.
The analyses of particulate levels and their characteristics are based on both the
raw data and the smoothed curves.
In order to conserve space, the very few raw data values exceeding 500 p.g/m3
were plotted as 500 ng/m3 in the graphical routine. Actual values were used,
however, in calculations and in construction of the smoothing curves.
The irumber of valid samples -within a valid year ranged between 20 and 26.
Since samples had been collected on random days within the sampling periods, pin-
pointing the exact day of sampling was impractical on a small-scale graph.
-------
Consequently, the raw data points within that year were graphically equispaced
throughout the year.
Invalid years required special attention. The computer was programmed to
estimate raw data values for invalid years to provide expediency in the graphical
routine and to aid in detection of characteristic patterns. The expected particulate
level for an invalid year was estimated and plotted as the level for the first sample
during that year. The graphs were completed by drawing connecting lines from
this estimate to the last sample value in the preceding valid year and to the first
sample value in the next valid year. This estimate had little effect on calculations;
any reasonable estimate would have been satisfactory. This estimate, however,
should correspond to expected particulate levels.
A time-series analysis emphasizes the trends and patterns of the data. In
this approach, the site samplings are viewed as a sequence of events rather than
merely a mass of data. The continuity of smoothed curves allows a realistic
estimate of what particulate-pollution levels would be if sampled continuously
rather than once every 2 weeks. These curves also facilitate a comparison of
trends in particulate concentrations at a number of sites, which is more meaningful
than a comparison of yearly averages.
The smoothed curves, illustrating the seasonal and long-term characteristics
of particulate levels, were constructed for all sampling sites included in the study
and were easily adaptable to those sites with invalid data. The flexibility of this
method permitted the inclusion of a large number of sites; thus, certain general
conclusions could be drawn. Another advantage of smoothing curves is that an
opportunity for visual analysis of the characteristics of particulate patterns through-
out the 10-year period is afforded.
The mathematical concept of curve-smoothing is not an elementary one, but
the visual results can be demonstrated easily. Intuitive methods of drawing a
smooth curve through raw data points, such as the use of a. French curve, are
subjective and produce varying results. In contrast, mathematical techniques
are objective, treating all data on the same basis. Since curve smoothing is an
innovation in analysis of air pollution data, a. description of the process is given.
The formulas used in this study were developed in the field of actuarial mathe-
matics, where curve smoothing is referred to as graduation. Graduation is the
process of securing a. smooth series which adequately represents basic character-
istics from an irregular series of raw data values.
The appropriateness of any graduation depends upon a balance between two
interdependent criteria: (1) Smoothness, and (2) fit, or consistency with raw data
values. For ultimate smoothness, a straight line approximates all data values;
for ultimate fit, raw data values are not graduated at all. Generally, an increase
in the smoothness of a curve is accompanied by a reduction in the fit, and vice
versa. Since no graduation can provide simultaneously both the best possible fit
and the best possible smoothness, a compromise must be reached. Thus, the best
graduation is the one that provides the best combination of these two characteris-
tics for the purpose at hand. Applied to the NASN suspended particulate data, an
emphasis on smoothness produces the long-term trend curves in the lower graph in
each figure, whereas more emphasis on fit produces the seasonal-pattern curves
in the upper graph in each figure.
CHARACTERISTICS OF PARTICULATE PATTERNS
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A number of different graduation methods are available, ranging from simple
graphic methods to complicated mathematical techniques. The choice of any one
method depends on the extent and form of the raw data, the purpose for which the
graduation is used, the relative emphasis to be given each of the qualities, and the
graduator's technical familiarity with the data and the technique. The Whittaker-
Henderson Type A Difference Equation Method used in this analysis is formulated
as the minimization of an expression combining weighted measurements of fit and
smoothness .
The Whittaker-Renders on formula is essentially a finite-difference function
permitting variation of the blend between fit and smoothness by variation of the
parameter 'a', a positive number fixing the relative weight of these qualities.
Small values of 'a1 emphasize fit, whereas large values emphasize smoothness.
Since a detailed understanding of the method requires a knowledge of numerical
analysis and finite differences, and since the formula and its derivation are not
necessary for interpreting the graphs, further details are omitted here. *
The graphs in Figure 1 demonstrate the graduation effects of different values
of this parameter. The value a=3 is used in plotting seasonal patterns because the
graduated curve retains fluctuations characteristic of monthly variations yet
eliminates random undulations resulting from individual measurements. Since the
long-term curves require emphasis on general tendencies over the 10-year period
rather than short-term patterns in the raw data, the value a=20 is used in plotting
long-term curves.
Although the mathematical derivation of the Whittaker-Henders on method is
complicated, the graphs produced with this method are straightforward illustrations
of patterns and trends in particulate pollution.
*A mathematical development of the Whittaker-Henderson Type A Formula may be
found in references 7 and 8. This derivation is summarized in reference 9.
Development and Application of the Method
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1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
1957 1958 1959 1960 1961 1962 1963 1964 1
1957 1958 1959 1960
1962 1963 1964 1965 1966
Figure 1 Variations in curves for selected values of graduation parameter.
CHARACTERISTICS OF PARTICULATE PATTERNS
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1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
Figure 1 (continued). Variations in curves for selected values of graduation parameter.
Development and Application of the Method
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DISCUSSION
The smoothed curves presented here adequately illustrate patterns and trends
in the data. The reader should thoroughly examine the graphs for sites of specific
interest to him. A detailed analysis of all graphs is precluded in this discussion
by the lack of sufficient knowledge of local environment and meteorology and by the
large number of the graphs. Rather than attempt a detailed scrutiny of each graph,
the sites have been categorized according to some of the prominent features of the
graphs.
Individual graphs were used, however, to follow up the previously mentioned
1961 drop study. 4 Seasonal patterns of the urban sites included in that study show-
ed that particulate concentrations during the 1961-6Z winter were, in general, quite
low. In contrast, the particulate concentrations at many urban sites during the
1960-61 winter were relatively high. This additional information may redefine the
1961 drop in annual mean concentrations as the result of a. drop in winter particu-
late concentrations.
Study results are partially summarized in Tables 1 and 2, which categorize
center-city urban and nonurban sites according to their seasonal patterns and
long-term trends. These tables also present for each site the geometric means
and the geometric standard deviations for the two consecutive 5-year periods since
1957. The different characteristics of each individual site in these tables should
be considered together. For example, the mean particulate concentrations must
not be ignored in a discussion of trends, because the two are interrelated. The
seriousness of the particulate problem depends ultimately on concentration levels.
Thus, long term changes may be more significant for a site with high mean con-
centrations than for a site with consistently low levels. Frequent reference to
the graphs will illustrate the results presented in these tables.
The geometric standard deviations can provide important information about the
sampling sites. Large geometric standard deviations can indicate, for example,
an irregular distribution of particulate sources. Sources maybe scattered in such
a way that particulate levels are strongly dependent on wind direction. If major
sources of particulate pollution are concentrated in one area of the city, winds from
that direction may produce high particulate levels while winds from other directions
are likely to result in low levels. The final effect of such a situation is a wide de-
viation in particulate concentrations, such as that observed in Charleston, West
Virginia, where wide deviations are due primarily to changes in wind direction and
wind velocity.
In addition, the combination of a small geometric standard deviation and a high
mean concentration may be due to distribution. Such a combination may result if
the sources of pollution are distributed about the sampling site in such a way that
wind direction has little effect on particulate concentrations. Frequent high levels
can result also if the sampling site is in line with the prevailing wind direction from
the sources.
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Table 1. CENTER-CITY URBAN CHARACTERISTICS OF SUSPENDED PARTICLES
Site
Birmingham, Ala.
Anchorage, Alaska
Phoenix, Ariz.
Little Rock, Ark.
Los Angeles, Calif.
San Diego, Calif.
San Francisco, Calif.
Denver, Colo.
Hartford, Conn.
New Haven, Conn.
Wilmington, Del.
Washington, D. C.
Tampa, Fla.
Atlanta, Ga.
Honolulu, Hawaii
Boise, Ida.
Chicago, 111.
East Chicago, Ind.
Indianapolis, Ind.
Des Moines, Iowa
Wichita, Kan.
New Orleans, La.
Portland, Me.
Baltimore, Md.
Boston, Mass.
Detroit, Mich.
Minneapolis, Minn.
Jackson, Miss.
Kansas City, Mo.
St. Louis, Mo.
Helena, Mont.
Omaha, Nebr.
Newark, N. J.
Albuquerque, N. M.
New York City, N. Y.
Charlotte, N. C.
Bismarck, N. D.
Cincinnati, Ohio
Cleveland, Ohio
Columbus, Ohio
Dayton, Ohio
Youngstown, Ohio
Portland, Ore.
Philadelphia, Pa.
Pittsburgh, Pa.
Providence, R. I.
Columbia, S. C.
Sioux Falls, S. D.
Chattanooga, Tenn.
Nashville, Tenn.
Dallas, Tex.
Houston, Tex.
San Antonio, Tex.
Salt Lake City, Utah
Burlington, Vt.
Norfolk, Va.
Seattle, Wash.
Charleston, W. Va.
Milwaukee, Wis.
Cheyenne, Wyo.
1957-1961
Geom.
mean,
ug/m3
125.6
83.4
206.5
74.1
164.2
85.4
65.3
137.4
88.8
84.8
175.3
106.8
85.9
99.4
48.8
104.7
179.4
176.7
157.1
150.2
86.3
88.4
86.3
131.5
131.3
134.1
94.4
71.7
140.7
159.7
54.7
106.1
97.2
183.5
167.9
114.4
80.0
124.6
154.5
129.0
113.0
137.7
75.5
162.3
160.3
100.1
106.8
81.3
190.1
126.6
91.4
104.8
105.9
105.5
50.6
95.9
79.4
171.2
139.4
42.0
Geom.
std.dev.,
yg/m3
1.85
2.27
1.58
1.63
1.56
1.54
1.69
1.64
1.59
1.47
1.64
1.56
1.39
1.56
1.50
1.53
1.39
1.70
1.35
1.56
1.59
1.37
1.55
1.51
1.45
1.51
1.75
1.60
1.50
1.58
2.04
1.62
1.63
1.71
1.48
1.59
1.77
1.45
1.51
1.51
1.49
1.56
1.77
1.52
1.73
1.54
1.41
1.75
1.55
1.57
1.71
1.64
1.71
1.64
1.53
1.49
1.62
2.20
1.49
1.69
1962-1966
Geom.
mean,
Mg/m3
124.5
65.9
165.7
87.0
124.5
76.3
60.0
125.1
95.6
91.9
131.1
87.2
84.1
93.4
38.7
80.3
130.4
183.7
148.8
116.8
88.8
85.2
70.7
130.3
125.3
135.3
74.8
69.1
129.3
131.1
48.7
107.3
103.8
114.6
164.9
101.3
78.7
129.2
119.4
108.1
117.2
136.1
85.7
155.5
150.6
106.9
73.2
64.6
154.4
116.3
91.6
94.3
72.0
108.3
56.8
96.7
68.2
169.0
120.0
33.7
Geom.
std.dev.,
Mg/m3
1.68
2.31
1.73
1.74
1.60
1.53
1.60
1.54
1.58
1.52
1.41
1.45
1.44
1.49
1.37
1.53
1.47
1.51
1.41
1.58
1.56
1.44
1.57
1.49
1.46
1.63
1.50
1.46
1.48
1.44
1.73
1.49
1.54
1.70
1.56
1.62
1.94
1.50
1.53
1.48
1.64
1.56
1.93
1.41
1.55
1.48
1.50
1.64
1.50
1.57
1.56
1.47
1.52
1.63
1.61
1.52
1.51
2.07
1.63
1.72
Long-term
trend
No change
Down0
DownC
Upb
Downc
Downb
No change
No change
No change
No change
DownC
Downc
No change
No change
Downc
Downc
Downc
No change
No change
Downc
No change
No change
Downc
No change
No change
No change
Downc
No change
No change
DownC
No change
No change
No change
DownC
No change
Downb
No change
No change
DownC
Downc
No change
No change
No change
No change
No change
No change
DownC
DownC
DownC
No change
No change
Downb
Downc
No change
Upb
No change
Downc
No change
DownC
DownC
Change in
geometric
standard
deviation
Downb
No change
No change
UpC
No change
No change
No change
No change
No change
No change
Downc
Downb
No change
No change
No change
No change
Upb
Downb
No change
No change
No change
Upb
No change
No change
No change
Upb
Downc
Downb
No change
Downc
Down0
Downb
No change
No change
No change
No change
Upb
No change
No change
No change
Upb
No change
Upb
Downb
Downc
No change
No change
Downb
No change
No change
Downb
Downc
Downc
No change
No change
No change
Downb
No change
Upb
No change
Seasonal
patterna
Urban
Nonurban
Urban
None
Urban
Urban
Urban
Urban
Possible urban
Unusual
Urban
None
None
None
Urban
Urban
None
None
None
Unusual
None
None
None
Urban
Urban
None
None
None
Possible urban
None
Unusual
Unusual
None
Possible urban
None
Urban
Unusual
Urban
None
Unusual
None
Unusual
Unusual
None
None
None
None
None
None
Possible urban
Possible urban
None
Urban
Urban
Nonurban
None
None
Urban
None
Nonurban
Definitions for seasonal patterns can be found in the text.
"Statistically significant; categories are explained in the text.
cHighly significant statistically; categories are explained in the text.
10
CHARACTERISTICS OF PARTICULATE PATTERNS
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Table 2. NONURBAN CHARACTERISTICS OF SUSPENDED PARTICLES
Site
Grand Canyon Pk.,
Ariz.
Montezuma Co., Colo.
Kent Co., Del.
Butte Co., Idaho
Parke Co., Ind.
Delaware Co., Iowa
Acadia Nat'l Pk., Me.
Calvert Co., Md.
Glacier National Pk.,
Mont.
Thomas Co., Nebr.
White Pine Co., Nev.
Coos Co., N. H.
Cape Hatteras, N. C.
Ward Co., N. D.
Cherokee Co., Okla.
Clarion Co., Pa.
Washington Co., R. I.
Richland Co., S. C.
Orange Co., Vt.
Shenandoah Nat'l Pk.,
Va.
1957-1961
Geom.
mean,
pg/m3
16.7
11.3
58.1
18.6
54.3
36.1
24.1
37.6
11.0
22.3
10.9
16.3
31.5
20.0
38.0
38.6
30.2
31.0
38.5
29.8
Geom.
std. dev.,
|Ug/m3
2.42
2.15
1.44
2.08
1.43
2.02
1.83
1.63
2.63
1.89
2.61
1.61
1.41
2.22
1.64
1.67
2.07
1.61
1.43
1.53
1962-1966
Geom.
mean,
Ug/m3
1S.5
12.2
56.3
13.6
49.3
36.3
22.3
39.1
13.7
19.4
10.0
19.6
48.4
31.8
45.4
37.1
37.4
32.9
36.7
30.1
Geom.
std. dev.,
jug/m3
2.11
2.60
1.54
1.91
1.56
1.72
1.83
1.46
2.50
2.04
2.59
1.77
1.81
2.19
1.63
1.69
1.99
1.56
1.59
1.59
Long-term
trend
No change
No change
No change
Downc
No change
No change
No change
No change
Upb
No change
No change
Upt>
UpC
UpC
Upb
No change
Upb
No change
No change
No change
Change in
standard
geometric
deviation
Downb
Upb
No change
No change
Upb
Downc
No change
Down13
No change
No change
No change
Upb
UpC
No change
No change
No change
No change
No change
Upb
No change
Seasonal
pattern4
Nonurban
Nonurban
Nonurban
Nonurban
None
None
Possible nonurban
Possible nonurban
Nonurban
Nonurban
Possible nonurban
None
None
Nonurban
None
None
Nonurban
None
Possible nonurban
Nonurban
Definitions for seasonal patterns can be found in the text.
^Statistically significant; categories are explained in the text.
cHighly significant statistically; categories are explained in the
text.
At a number of sites, for example, Cape Hatteras, North Carolina, and San
Antonio, Texas, both deviations and mean concentrations changed significantly
from one 5-year period to the next. This suggests that the nature of sources has
changed. A sustained change in the meteorology is improbable; meteorology in an
area is considered to be a random factor and has been accounted for by the smooth-
ing technique. The only factors that can be expected reasonably to change are the
emissions and the distribution of sources. Such factors can be affected by instal-
lation of controls, opening or closing of plants, and changes in sampler location.
Certain combinations of characteristics at a few sites are puzzling. At
Delaware County, Iowa, and at Helena, Montana, the mean changed little while the
deviation changed significantly. At the present time, no explanation can be offered
for these phenomena.
Discussion
-------
Although standard statistical measures, such as those discussed above, are
useful in this study, data also were analyzed by less conventional methods. Ine
graduated curves themselves yielded much information. For example, the short-
term graduated curves were examined for evidence of seasonal patterns over the
10-year period. The criteria for determining seasonal patterns were straight-
forward but subjective. Typical patterns had to be observed in at least 6 years at
urban sites and 5 years at nonurban sites to be considered definite patterns. If a
site had only 5 years of typical patterns (4 years for nonurban sites), it was
categorized as having a possible seasonal pattern. Sites demonstrating a typical
seasonal pattern were classified as unusual. In Tables 1 and 2, all sites are
classified as to seasonal patterns on the basis of these criteria.
Typical patterns for urban locations are markedly different from those patterns
for nonurban locations. Urban sites generally are characterized by higher levels
of particles in the winter than in the summer; nonurban sites, in contrast, general-
ly are characterized by higher levels in the summer than in the -winter. A major
factor contributing to the higher particulate levels during the winter months at
many urban sites is the use of large amounts of fuel for space heating. In these
cases, the urban particulate pattern masks the nonurban or background particulate
pattern. In those southern cities where gas is burned rather than coal, higher
winter concentrations are less prevalent than in coal-burning cities.
A few urban sites were characterized by typical nonurban seasonal patterns.
The locations of these sites in remote sections of the country may partially explain
this phenomenon. For example, the pattern at Anchorage, Alaska, which would be
expected to be influenced by space heating in the winter, resembles the pattern at
typical nonurban sampling stations. This situation may be due to the facts that fuel
oil is used predominantly for space heating and that construction declines during
the winter months. The increased concentrations in the summer months are due
largely to the dust raised on the roads and to the resumption of construction.
The relatively high levels at nonurban sites during the summer can be partial-
ly attributed to the increases in human activities, such as farming, travel, and
vacationing. Also, since summer months are generally drier than other months,
more pollen and wind-blown dust (as opposed to man-made pollution) are to be ex-
pected during the summer months.
Sites have been classified on the basis of long-term trends, as well as season-
al patterns. To determine these trends, the geometric mean of the first 5-year
period, 1957 through 1961, was compared with the geometric mean of the second
5-year period, 1962 through 1966, and the difference was evaluated statistically.*
Since no nonurban sites had valid data for 1957, the long-term trend classifications
in Table 2 are based on comparisons of the 4-year period 1958 through 1961 to the
5-year period 1962 through 1966.
The five categories established for this classification are:
1. Highly significant downward trend Statistically significant at the a 0.01
level.
*Since the suspended particulate data are generally assumed to be log-normally
distributed, a two-tailed "t" test was used after a logarithmic transformation had
been performed on the original data. Serial correlation should not alter the
results of the "t" test significantly.
12 CHARACTERISTICS OF PARTICULATE PATTERNS
-------
2. Significant downward trendStatistically significant at the a O.I level.
3. No significant change Not statistically significant at the a 0. 1 level.
4. Significant upward trend Statistically significant at the a 0. 1 level.
5. Highly significant upward trend-Statistically significant at the
-------
industry, commerce, and population have continued to grow, much of this expan-
sion has been occurring away from the center-city area. Generally suburban areas
are growing at a faster rate than center-city areas, and industry has begun to move
to city outskirts. Basic changes in industrial processes and conversion to cleaner
fuels for space heating are partially responsible for the decline. Control devices
for industry certainly have contributed to the reduction of levels since 1957. In
some communities, the lower levels are attributable in part to increased effective-
ness of local air pollution control programs . Although the 1963 NASN study suggests
that anomalies in meteorological patterns may affect particulate levels, they do
not explain these downward trends. As mentioned previously in this discussion,
such peculiarities are smoothed out by the graduated curves and have little effect
on the graduated values.
Increased traffic density may have caused the rise in particulate levels at
some sites. Automobiles and buses are still chief modes of transportation within
the center-city. Many of the larger cities, such as Chicago, where particulate
levels decline, probably were affected little by increased traffic density in the past
10 years, because their center-city roadways were already saturated by 1957.
Demolition and construction associated with urban renewal and expressway projects
also may contribute to higher levels at some urban sites.
Many nonurban sites display long-term trends markedly different from those
of urban sites. As is shown in Table 3, only 1 of the ZO nonurban sites had a sig-
nificant decrease in particulate levels, while 13 sites had no significant change.
At the remaining six sites, particulate levels increased. Few sizable decreases
were expected because the mean levels were very small in 1957. In fact, 30 per-
cent of the nonurban sites had significant increases in particulate concentrations.
Again the movement of industry to city outskirts and to nonurban locations
partially explains the rise in particulate levels at those places. The great amount
of roadway construction over the past 10 years may have affected particulate levels.
Improved and extended highways, especially limited access high speed thorough-
fares, have encouraged such widespread traveling in recent years that practically
no nonurban site escapes the influence on particulate levels of automobile emissions.
An increase due to these emissions should be gradual as the roads are used more
and more each year; the graduated long-term curves for many nonurban sites do
illustrate such a gradual rise.
If urban sprawl results in a decline in particulate levels at center-city sites,
it should produce a corresponding increase in the levels in nonurban areas, there-
by causing a shift in the nationwide pollution problem. Increases in construction,
commerce, and traffic density associated with urban sprawl may cause the long-
term rise in particulate levels. For example, at Cape Hatteras, North Carolina,
according to the comparison of 5-year means, a highly significant increase in mean
levels has occurred since 1957. This site, with by far the largest increase among
nonurban sites, illustrates the effects of many contributing factors. Cape Hatteras
followed a typical nonurban pattern until 1963, when an upward trend began. In
1963, home construction near the sampler began to increase rapidly. In the vicini-
ty of the site, more business concerns have been established, and automobile traf-
fic has risen sharply. In addition, a State park was opened nearby.
The situation at Cape Hatteras also illustrates the ambiguities of the classifi-
cations "urban" and "nonurban. " Cape Hatteras is no longer a truly nonurban site.
14 CHARACTERISTICS OF PARTICULATE PATTERNS
-------
As another example, the sampling site in Washington County, Rhode Island, al-
though considered nonurban, is surrounded by the densely populated area of the
northeastern seaboard and exhibits some characteristics typical of urban sites. On
the other hand, Cheyenne, Wyoming, an urban site, has a lower 10-year geometric
mean, 36 ug/m , than many nonurban sites and a clearly nonurban seasonal pattern.
In addition to general characteristics of particulate patterns, in rare instances,
the graphs reveal some abnormally high values. For example, when a dust storm
swept over the Ward County, North Dakota, site in March of 1963, the station meas-
ured a. particulate level of 843 ug/m^ above the mean for that station over the 10-
year period. Anchorage, Alaska, in November of 1958, recorded a level of 487
Hg/m^ This level, largely due to a. volcanic eruption, was 411 |j.g/m3 above the
site's 10-year mean. Most of these unusually high spikes are due to stagnations,
dust storms, nearby construction, or other unusual conditions.
Discussion
-------
SUMMARY
The Whittaker-Henderson smoothing curves as applied here are a new ap-
proach to the detection of seasonal and long-term patterns in particulate levels.
Sites have been classified according to seasonal patterns and long-term trends.
This study has established a. typical seasonal pattern for urban sites, characterized
by high winter levels and low summer levels. In contrast, relatively small geo-
metric means, low winter levels, and high summer levels typify nonurban season-
al patterns. At both urban and nonurban sites, spring and fall appear to be tran-
sition periods for particulate levels. Significant decreases in particulate levels
were detected for many center-city urban sampling sites. The curves generally
reveal a slight increase in particulate concentrations at nonurban sites from 1958
to 1966.
17
-------
TREND IN TOTAL SUSPENDED PARTICULATES
USING WHITTAKER-HENDERSON TYPE A
SMOOTHING FORMULAS, 1957-1966
PART I. URBAN SITES
19
-------
[SI
O
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1957
1958
1959
1960
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YEAR
1963
1964
1965
Figure 2- Birmingham, Alabama.
-------
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Figure 3. Anchorage, Alaska.
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Figure 4. Phoenix. Arizona.
1963
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-------
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1957
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100
1957
1958
1959
1960
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YEAR
1963
1964
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Figure 5. Little Rock, Arkansas.
-------
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1958
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Figure 6. Los Angeles, California.
1963
1964
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1966
-------
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1957
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Figure 7. San Diego, California.
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Figure 8. San Francisco, California.
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Figures. Denver, Colorado.
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Figure 10. Hartford. Connecticut.
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1957
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1957
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1963
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Figure 11. Mew Haven, Connecticut.
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Ficiure 12. Wilmington, Delaware
1963
1964
1965
1966
-------
SOOr
SEASONAL
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1957
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1966
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1957
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Figure 13. Washington, D. C.
-------
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Figure 14- Tampa, Florida.
1963
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Figure 15. Atlanta, Georgia.
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1957
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LONG TERM
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Figure 16. Honolulu, Hawaii.
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13
400
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1957
500
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1958
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1958
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1959
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1963
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YEAR
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1966
Figure 17. Boise, Idaho.
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1957
1958
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1966
Figure 18. Chicago, Illinois.
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1963 1964 1965 1966
1957 1958
1959
1960
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Figure 19. East Chicago, Indiana.
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400
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1957 1958 1959 1960
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YEAR
1963
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1965 1966
Figure 20. Indianapolis, Indiana.
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500,
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Figure 21. Des Monies, Iowa.
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Figure 22. Wichita. Kansas.
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300h
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1957
1958
1959
1960
1961 1962
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1963
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1966
Figure 23. New Orleans, Louisiana.
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1957
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Figure 24. Portland, Maine.
-------
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1957
1966
Figure 25. Baltimore, Maryland.
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1957
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1957
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1966
Figure 26- Boston, Massachusetts.
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1957 1958
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YEAR
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Figure 27. Detroit, Michigan.
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1957 1958
1959
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Figure 28. Minneapolis, Minnesota.
-------
13
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300
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1965
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LONG TERM
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1957
1958
1959
1960 1961 1962
YEAR
1963
1964
1965
Figure 29. Jackson, Mississippi.
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1957
1958
1959
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1966
Figure 30. Kansas City, Missouri.
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1957 1958 1959 1960 1961 1962 1963 1964
1965 1966
Figure 31. St. Louis, Missouri.
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500
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1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 32. Helena, Montana.
-------
t-
a:
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Q
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400
300
200
1957 1958
1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
Figure 33. Omaha, Nebraska.
-------
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a
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1957
1958
1959
1960
1961 1962
YEAR
Figure 34. Newark, New Jersey.
1963
1964
1965
1966
-------
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=
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Q
LU
D
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111
Q.
500
400h-
1957 1958
1959
1960
1961 1962
YEAR
1963 1964
1965 1966
Figure 35. Albuquerque, New Mexico.
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173
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1957
1958
1959
1960
1961 1962
YEAR
Figure 36. New York City, New York.
1963
1964
1965
1966
-------
500
LLJ
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O
Q.
Q
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Z
LU
Q.
1957 1958
1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
Figure 37. Charlotte, North Carolina.
-------
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500
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100
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 38. Bismarck, North Dakota.
-------
a
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500
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Q
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01
Q.
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1957 1958 1959 1960 1961 1962 1963 1964
1965 1966
Figure 39. Cincinnati, Ohio.
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LU
Q
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1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 40. Cleveland. Ohio.
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p
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Q
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500,
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1957 1958 1959 1960 1961 1962 1963 1964 1965
1966
Figure 41. Columbus, Ohio.
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500r
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300>
200
100p
500
SEASONAL
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1966
Figure 42. Dayton, Ohio.
-------
a
er-
as
D
Tl
P
s
m
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o
a
uj
a
z
UJ
Q_
O
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500
400|
200
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 43. Youngstown, Ohio.
-------
o
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ra
so
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o
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tr
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a.
a
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a
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HI
0_
500
4001
300K
1001
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 44> Portland, Oregon.
-------
500
r
13
V
r
UJ
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o
Q.
Q
HI
Q
Z
HI
Q.
H
O
400
300
200
1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
Figure 45. Philadelphia, Pennsylvania.
-------
o
so
o
H
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111
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1957 1958 1959 1960 1961 1962 1963 1964 1965
Figure 46. Pittsburgh, Pennsylvania.
-------
G
LLJ
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O
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Q
III
Q
Z
LU
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<
O
SOOi
4001
300 (
200|
1001
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965 1966
Figure 47. Providence, Rhode Island.
-------
500 r
SEASONAL
400
O
o
H
O
O
r
LU
_l
O
D.
Q
LU
Q
Z
LU
Q_
300-
200
100
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 48- Columbia, South Carotin
-------
LU
_l
o
Q
111
Q
Z
Ul
Q.
h-
o
500
400\
1962 1963 1964 1965
1957
Figure 49. Sioux Falls, South Dakota.
-------
O
O
H
H
O
en
O
n
0
o
G
IT1
K
"0
E
01
a.
CO
HI
O
Q.
Q
UJ
Q
Z
LU
Q.
H
O
H
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 50. Chattanooga, Tennessee.
-------
\-
tr
<
o.
Q
III
Q
LU
Q.
<
O
500
4001
3001
200|
1963 1964 1965 1966
1958 1959 1960 1961
1957
1963 1964 1965 1966
Figure 51. Nashville, Tennessee.
-------
SO
>
O
O
Cfl
~a
>
so
J;3
O
f
13
111
_l
O
Q.
Q
HI
Q
2
UJ
Q.
500
400K
300h-
1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
100
Figure 52- Dallas. Texas.
-------
e
UJ
_i
o
Q
LU
D
Z
LU
Q-
500
400 H
300 H
200
1957
Figure 53- Houston, Texas.
-------
o
SC
SO
i-3
B9
SO
Cfl
O
TJ
so
G
"0
1-3
H
PI
50
E
.
01
LU
_J
O
Q.
Q
LU
O
HI
Q.
1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
Figure 54. San Antonio. Texas.
-------
s
LU
_l
O
Q
LU
Q
LU
a.
<
o
500
400 h-
300h-
200h-
100
100
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 55. Salt Lake City, Utah.
-------
500r
SEASONAL
400k
300H
o
>
33
>
O
H
M
O
en
O
"3
H
o:
Q
HI
Q
CL
v>
D
_l
<
200^-
100
1957
1958
1959
1960
1961
1962
1963
1964
500r
400f-
300 ^
LONG TERM
O
f
t-3
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 56. Burlington, Vermont.
-------
G
o-
T3
£
o
B
SOOr
400h-
SEASONAL
LU
_j
o
300 h-
200
100
Q.
O
HI
o
z
UJ
Q.
1957
1958
1959
1960
1961
1962
1963
1964
1965
500
400H
2 300 r
1957
1966
1965 1966
Figure 57. Norfolk, Virginia.
-------
SOOr
SEASONAL
400h
o
S
O
M
O
CO
13
O
M
B
3>
UJ
_J
O
Q
HI
Q
Z
HI
Q_
<
O
300f
200 H
iooh
500i
400
300
200
100
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
LONG TERM
J II (I
i /I I t I
ft/rv^JWkJLUiJ^
\r
1957
1958
1959
1960
1961 1962 1963
YEAR
1964
1965
1966
Figure 58. Seattle, Washington.
-------
G
SOOi
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 59. Charleston, West Virginia.
-------
JO
o
H
M
S
O
OQ
"0
>
to
3
o
r
>
1-3
W
13
1957 1958
1959
1960
1961 1962
YEAR
Figure 60. Milwaukee. Wisconsin.
1963 1964
1965
1966
-------
TREND IN TOTAL SUSPENDED PARTICULATES
USING WHITTAKER-HENDERSON TYPE A
SMOOTHING FORMULAS, 1957-1966
PART II. NONURBAN SITES
79
-------
00
o
O
1-3
K
si
o
oo
O
1-9
ea
H
1-3
ra
HI
_l
o
D
111
D
_l
<
500
400
300
200
100
0
1957
500
400
300
200
100
Vf^^
V
1
SEASONAL
1958
1959
1960
1961
1962
1963
1964
1965
1966
1
LONG TERM
V^
si
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 61. Cheyenne, Wyoming.
-------
TJ
s
400
300
1
SEASONAL
200
III
_1
o
D
111
D
LU
Q.
100 _
500
400
300
200
1957
1958
1959
1960 '
1961
1962
1963
1964
1965
1966
I
LONG TERM
100
A,
£A4
1957
1958
1959 1960 1961 1962 1963
YEAR
Figure 62. Grand Canyon Park, Arizona.
1964
1965
1966
-------
o
s
o
O
"9
"0
W
g
O
G
H
fl
HI
_l
o
Q
HI
D
z
IJJ
Q.
SEASONAL
400f
300{-
1957
500,
1958 1959
I
1960
1961
1962
^
1963 1964 1965 1966
LONG TERM
1963 1964
T
400
2 soof
200
100
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 63. Montezuma County, Colorado.
-------
SOOr
SEASONAL
a
(0
r
300K
E 200(-
UJ
_i
o
Q-
D
Ul
D
100
1957
500 r
CL
CO
3 400)
1958
1959
1960
1961
1962
1963
1964
1965
LONG TERM!
1966
i-
o
300 H
A
1957
1958 i
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 64. Kent County, Delaware.
-------
500r
1
SEASONAL
400
300
200
o
i-3
K
H
O
ID
_J
O
a.
Q
LU
Q
LU
Q.
O
I-
100
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
500
I
LONG TERM
400
300
200
O
1-3
1-3
H
100_
JLa/
-M=
1957 1958 1959 1960 1961 1962
YEAR
Figure 65. Butte County, Idaho.
1963
1964
1965
1966
-------
s
I
I
LU
_J
o
D
LU
D
Z
UJ
D.
iooU
500r
400f
3001
200^
1964
1965
1966
LONG TERM
1957
1958
1959
1960
1961 1962
YEAR
Figure 66. Delaware County, Iowa.
1963
1964
1965
1966
-------
500r
400f
300f
1 r
SEASOMAL
o
1-9
H
58
O
en
PS
H
O
M
58
III
_j
O
Q
111
Q
Z
III
Q.
O
1-
200t-
100U
500f
400f
300f
200f
10(H
1957
19581
1959
1960
1961
1962
1963
1964
1965
1966
LONG TERM
i
A/vA^oM
1 V*
' 1
1957 1958 1959 1960 1961 1962
YEAR
Figure 67. Parke County, Indiana.
1963
1964
1965
1966
-------
(0
TO
Q
111
D
Z
LLJ
Qu
CO
400h-
300^-
200H
100t
-IQ57
500f
400}
H 300H
200^
100t-
957
SEASONAL
1958
1959
1960
1961
1962
1963
1964
1965
1966
LONG TERM
A A . X
, v VXA/VY^ r
.K A JU
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
00
-J
Figure 68. Acadia National Park, Maine.
-------
soor
400-
300-
200-
SEASONAL
s
w
a
H
O
w
O
"3
fl
S3
r
1-3
H
CL
Q
UJ
Q
UJ
0_
<
O
100
500
400
300
200
100
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
\
LONG TERM
v
1957
1958
1959
1960
1961 1962
YEAR
Figure 69 Calvert County, Maryland.
1963
1964
1965
1966
-------
TJ
s
t-
CC
D
LU
Q
z
111
a.
400
300
200
100
1957
1957
1
SEASONAL
1958
1959
1960
1961
1962
1965
1966
500
400
300
200
100
0
r r i i i i 1 i
LONG TERM
-
I
ji ..AH. ^ -"I « n- jWjL A-. ^/L-
1 : I-^"W !f 1 _ - 1 ~^^ rtcn^^'^' I^JtTl ^~ ^ ' ^~ * \*^*~ ' Ir^J
1958
1963
1964
1965
1966
YEAR
Figure 70. Glacier National Park, Montana.
-------
500r
SEASONAL
400-
300-
200
o
I
o
m
_i
o
Q
LLJ
D
Z
HI
Q_
100
500
400
1957 1958
1959
1960
1961
1962
1963
1964
1965
1966
1
LONG TERM
en
o
73
O
I-
300
200
SO
H
O
c
f
1-3
-3
ta
100
/W/uJW\A JwuA/l
1957 1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 71. Thomas County, Nebraska.
-------
CO
CO
DC
<
0.
Q
UJ
Q
Z
tu
CL
500
400{
300 f-
200
UJ 1001
_i
O
1957
500
400
SEASONAL
1958
1959
1960
1961
1962
1963
1964
1965
1966
<
O 300
200
100
LONG TERM
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 72. White Pine County, Nevada.
-------
sD
rxj
§
>
>
o
H
M
»
w
0
CO
O
hq
so
o
r
M
^0
H
P3
CO
£=
O)
i
CO
HI
_J
O
1-
cc
D-
D
HI
Q
Z
HI
CL
CO
^)
CO
_J
1
O
H
ouu
400
300
200
100
0
500
400
300
200
100
o
I I I I I I I I I
SEASONAL
-
- -
«
, ^^^^^M^^^^
1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
I I I I I I
LONG TERM
*
^ AA* AA. - , * AA K A ^^/lAA t\^ 1^ ^MUJU
1 *~ f1 ' v v VJ==-fA-'"u' ^ ^^/^^ '* ''^-'V \t \_J v LJ vy v ^""^-^_j uv -1 v ^ V"~y__^_j u v "T-^ "^ V
1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
YEAR
Pinuro "77 Pnnc Pnuntu IMauu Hnmnchiro
-------
SEASONAL
O
s
UJ
_l
o
a
LU
a
z
UJ
a.
in
400
300
200
100
0
500
400-
LONG TERIVl
300-
200-
100-
_L
1957
1958
1959
1960
1961 1962
YEAR
1963
1964 1965
1966
Figure 74. Cape Hatteras, North Carolina.
-------
o
o
H
S
5
H
o
on
O
"3
13
O
G
11
H
1-3
H
UJ
_l
O
CL
Q
HI
Q
O
500r
400f
300^
200\
SEASOMAL
500 r
400 {
300 f
200 f
100^-
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
LONG TERH/1
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 75. Ward County, North Dakota.
-------
CD
S
UJ
_J
(J
Q.
D
111
O
z
HI
Q.
500,
400f-
LONG TERM
400 f-
300f
100 f
1957 1958
1959
1960
1961 1962
YEAR
1963
1964
1965 1966
Figure 76. Cherokee County, Oklahoma.
-------
500r
1 'T
SEASONAL
400-
300
200-
O
o
1-3
£3
o
Crt
O
"3
13
S3
O
c-1
ea
fa
CO
Ul
Q.
D
LU
Q
Z
LU
Q.
<
O
500
400
300
200
100
1957
1958
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
LONG TERM
V/V
vv-1
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 77. Clarion County, Pennsylvania.
-------
500 r
SEASONAL
400 H
UJ
_i
o
Q-
Q
UJ
Q
LU
0.
en
200 r
100
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
500 r
400 f
LONG TERM
O
300f
200 f
100f
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 78. Washington County, Rhode Island.
-------
O
>
93
O
Hi
H
»
O
''I
T3
O
G
r
H
H
fl
H
H3
K
§
n
O
h-
ir
<
Q.
Q
HI
O
Z
UJ
CL
500 r
400 f
300f
200f
100f
SOOr
400f
300f
200f
100^
SEASONAL
W
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
LOIMG TERIW
A/
,/VA
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 79. Richland County, South Carolina.
-------
SEASONAL
"0
P3
I
r
CO
400^
300U
200
LLI
_l
o
i
cc
<
a.
D
in
a
in
D.
1001-
^v^W^Mn^
i * ^j ,
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
SOOr
400f-
LOIMG TERM
300^
200f
100f
LfVAAA A
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965 1966
Figure 80. Orange County, Vermont.
-------
o
o
500r
1
SEASONAL
400-
300
200
O
X
90
>
o
H
58
Hj
O
LU
_J
O
Q
ai
Q
UJ
CL
_l
<
100
1957 1958 1959
400
300
LONG TERM
B
58
g
O
ea
T3
i-3
H
B
§
en
200
100
1957
1958
1959
1960
1961 1962
YEAR
1963
1964
1965
1966
Figure 81. Shenandoah National Park, Virginia.
-------
REFERENCES
1. The Effects of Air Pollution. USD HEW, PSH Publication No. 1556, U. S.
Government Printing Office, Washington, D. C. , 1966. 18pp.
2. Tabor, E. C. , "High-Volume Air Sampling." Presented at the 7th Indiana Air
Pollution Control Conference, October 15-16, 1968, Purdue University,
Lafayette, Ind.
3. Air Pollution Measurements of the National Air Sampling Network, "Analysis
of Suspended Particulates , " USDHEW, Public Health Service, Division of Air
Pollution, Robert A. Taft Sanitary Engineering Center, Cincinnati, Ohio, 1965.
87pp.
4. McMullen, T. B., and R. Smith, The Trend of Suspended Particulates in
Urban Air: 1957-1964, USDHEW, PHS Publication No. 999-AP-19, 1965. 27pp.
5. McMullen, T. B. Personal communication.
6. Fair, Donald H. , George B. Morgan, and Charles E. Zimmer, Storage and Re-
trieval of Air Quality Data (SAROAD); System Description and Data Coding
Manual. USDHEW, Public Health Service, National Center for Air Pollution
Control, Cincinnati, Ohio, APTD 68-8, 1968. 47pp.
7. Miller, Morton D. , Elements of Graduation, Actuarial Society of America and
American Institute of Actuaries, Philadelphia, Pa., 1946. 76pp.
8. Henderson, Robert, "Method of Graduation," Actuarial Society of America,
Vol. XXV, 1924. pp. 29-39, 292-307.
9. Spirtas, Robert, and Howard J. Levin, "Patterns and Trends in Levels of
Suspended Particulate Matter at 78 NASN Sites from 1957 through 1966. " Pre-
sented at 62nd Annual Meeting, Air Pollution Control Association, New York,
N. Y., June 22-26, 1969.
101
-------
ERRATA
AP-61 - CHARACTERISTICS OF PARTICULATE PATTERNS,
1957-1966
Please make the following changes in your copy(s)
of subject report.
Page 12, paragraph 1, line 9, the next to last
sentence should read:
"Sites demonstrating atypical seasonal
patterns were classified as unusual."
Page 80:
The graph for Cheyenne, Wyoming, belongs
in the Urban section.
Page 101, reference 1:
PSH should be PHS.
------- |