Atmospheric Environment Vol. 27A, No. 16, pp. 2645-2668, 1993. 0004 6981/93 $6.00+0.00 

Printed in Great Britain. Pergamon Press Ltd 

A CHARACTERIZATION OF THE SPATIOTEMPORAL 

VARIABILITY OF NON-URBAN OZONE CONCENTRATIONS 

OVER THE EASTERN UNITED STATES 

BRIAN K. EDER* 

Atmospheric Sciences Modeling Division, Air Resources Laboratory, National Oceanic and Atmospheric 

Administration, Research Triangle Park, NC 27711, U.S.A. 

JERRY M. DAVIS 

Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, 

NC 27695, U.S.A. 

and 

PETER BLOOMFIELD 

Department of Statistics, North Carolina State University, Raleigh, NC 27695, U.S.A. 

(First received 6 October 1992 and in final form 12 April 1993) 

Abstract--The spatial and temporal variability of the daily 1-h maximum 0 3 concentrations over nonurban areas of the eastern United States of America was examined for the period 1985-1990 using principal 

component analysis. Utilization of Kaiser's Varimax orthogonal rotation led to the delineation of six 

contiguous subregions or "influence regimes" which together accounted for 64.02% of the total variance. 

Each subregion displayed statistically unique 03 characteristics and corresponded well with the path and 

frequency of anticyclones. When compared to the entire domain, the mid-Atlantic and south subregions 

observe higher mean daily 1-h maximum concentrations. Concentrations are near the domain average for 

the northeast and southwest subregions and are lowest in the Great Lakes and Florida subregions. The 

percentage of observations exceeding 120 ppb were greatest in the mid-Atlantic and southwest subregions, 

near the domain average in the northeast and south subregions, and lowest in the Great Lakes and Florida 

subregions. 

Examination of the time series of the principal component scores associated with the subregions indicated 

that Great Lakes and mid-Atlantic subregions tend to observe a stronger seasonal cycle, with maximum 

concentrations occurring during the last week in June and first week in July, respectively. The strength of this 

seasonality is weakened for the northeast and south subregions and its timing delayed, until the end of July 

and the first of August, respectively. The southwest subregion experiences a greatly diminished seasonality, 

with maximum concentrations delayed until the middle of August. The seasonality found in the Florida 

subregion is unique in both its strength and timing, as the highest concentrations consistently occur during 

the months of April and May. The time series were then deseasonalized and autocorrelations and spectral 

density estimates calculated, revealing that persistence is much more prevalent in the Florida (autocorrelation significant to a lag of 4 days), south (3 days) and southwest (3 days) subregions. Conversely, 

autocorrelations are only significant to a lag of one day in the northeast and two days for the Great Lakes 

and mid-Atlantic subregions. 

Key word index: Ozone, principal component analysis, influence regimes, time series analysis, persistence, 

seasonality, anticyclones. 

l. INTRODUCTION 

Traditionally, ozone (Oa) has been characterized as an 

urban-scale pollutant. More recently, however, it has 

been recognized by scientists as a regional (Logan, 

1989) and even global-scale phenomenon (Liu et al., 

1987), as high concentrations are routinely observed 

over vast, non-urban areas of most industrialized 

countries where forest retardation and crop injury are 

becoming growing environmental concerns (Lefohn 

• On assignment to the Atmospheric Research and Exposure Assessment Laboratory, U.S. Environmental Protection 

Agency. 

and Lucier, 1991). Daily maximum 0 3 concentrations 

in these areas are often comparable to those found in 

urban areas (Meagher et al., 1987) and daily average 

concentrations can even exceed urban concentrations 

due to a lack of nitrous oxide (NO) scavenging. 

As early as 30 years ago, Mukammal (1960) and 

later MacDowell et al. (1964) noted a direct correlation between high 03 concentrations and tobacco 

weather fleck, a malady responsible for the loss of 

millions of kilograms of tobacco in southern Ontario. 

More recently, Reich and Amundson (1985) have 

shown that exposure to 03 concentrations as low as 

50 ppb is sufficient to cause damage to certain species 

of vegetation. Additionally, the Office of Technology 

2645 
2646 B.K. EDER et al. 

Assessment (OTA, 1989), in a review of the effects of 

0 3 on crops in the eastern United States of America, 

determined that 03 reduces yields of economically 

important crops from between 1 and 20%, and that 

annual benefits of between $0.5B and $1.0B would be 

realized if rural 03 concentrations were reduced by 

25%. Ozone has also been hypothesized as being 

partially responsible for the decline of several tree 

species in the eastern United States of America, southern Canada and Europe (Aneja and Li, 1992; OTA, 

1989; Chevone and Linzon, 1988; Linzon, 1986). In a 

review paper, McLaughlin (1985) found that concentrations in the range of between 60 and 80 ppb were 

contributors to forest decline at many high elevation 

locations within these same areas. 

The importance of the contribution of non-urban 

(or regional) 03 concentrations toward understanding, predicting and reaching attainment of concentrations within urban areas is also well known (Angle and 

Sandhu 1986) and warrants investigation (Altshuller, 

1986; Chameides et al., 1988). In an examination of 

high 03 events in Atlanta, GA, Lindsay and Chameides (1988) found that urban 03 concentrations exceeded the National Ambient Air Quality Standards 

(NAAQS) level of 120 ppb only when there was a 

significant regional contribution (on the order of 

80-85 ppb). In a similar study of Detroit, MI, Kelly et 

al. (1986) found that on days with the highest concentrations (>100ppb), nearly half of the 0 3 was attributable to regional transport. 

Clearly an accurate, unbiased characterization of 

the spatiotemporal variability of 03 concentrations 

observed in non-urban areas would be helpful toward 

the development and implementation of standards 

designed to: (1) assist urban areas in reaching attainment of the NAAQS, and (2) mitigate any adverse 

effects on forest and crop productivity as well as 

ecosystem well-being. Attempts to characterize solely 

the spatial variability of 03 (involving a variety of 

statistical techniques) have been numerous and include: Logan (1989) who used cross-correlation coefficients to examine the spatial coherence among 18 

rural stations in the eastern United States of America; 

Lefohn et al. (1988) who used kriging, which is an 

interpolation technique, to characterize the spatial 

patterns of 03 in the southeastern United States of 

America, and Cox and Clark (1981), who used factor 

analysis in an examination of the spatial relationships 

between 21 urban sites in the eastern United States of 

America. While providing useful information concerning the spatial variability of 03, none of these 

methods allow the investigator to simultaneously 

characterize the spatial and temporal variability of 0 3 

as does the multivariate technique, Rotated Principal 

Component Analysis (RPCA), which is utilized in this 

study. 

RPCA allows us to segregate the eastern United 

States of America into "influence regimes" or subregions whose 0 3 concentrations exhibit unique, 

homogeneous characteristics, presumably in response 

to a commonality of forcing factors (i.e. meteorology, 

emissions, geography). Examination of these homogeneous characteristics can then be achieved using a 

variety of statistical analyses including time series 

analysis. This approach has been used successfully in 

the examination of other aerometric data, including 

SO 2- concentrations in precipitation (Eder, 1989) and 

SO2 ambient air concentrations (Ashbaugh et al., 

1984). The advantages of using RPCA are numerous 

(Cox and Clark, 1981; Crutcher et al., 1986; Eder et al., 

1987; Eder, 1989). Most importantly, this approach 

allows comparison of 03 characteristics (i.e. seasonal 

and annual distributions, trends) between subregions 

whose segregation is statistically and physically based. 

Currently, most studies base region segregation on 

either geopolitical boundaries (U.S. EPA, 1990), or 

boundaries determined a priori, such as Lefohn and 

Shadwick (1991), who compared 03 characteristics 

between regions whosd segregation was based on 

agricultural or forestry use. Secondly, since stations 

within subregions exhibit homogeneous O3 characteristics, this approach allows us to develop regionwide 

indicators that should provide meaningful insight into 

the variability of 03 in these subregions. And finally, 

the analysis of 03 characteristics and trends within 

these subregions will be based on an aggregation of 

data from many stations, as opposed to individual 

stations, thereby minimizing the effects of anomalous 

or even erroneous data often associated with a particular station. 

The purpose of this study is to provide an 03 

climatology through the characterization of the spatial and temporal variability of 0 3 concentrations 

found in non-urban areas of the eastern United States 

of America. Such analysis will hopefully act as a 

catalyst for a variety of interpretive studies designed to 

explicate these characteristics and their relationships 

with emissions and meteorology patterns. 

2. 0 3 DATA 

The data employed in this analysis were obtained from 

the Environmental Protection Agency's (EPA) Aerometric Information Retrieval System (AIRS). This 

database contains a multitude of hourly aerometric 

data, including 03 concentrations (measured in parts 

per billion--ppb), collected by local agencies at nearly 

800 stations nationwide. The data are monitored using 

either chemiluminescence analysers or ultra-violet 

photometers, and adhere to strict criteria established 

by the U.S. Environmental Protection Agency (U.S. 

EPA) (1985), which include: multipoint calibrations, 

independent audits and data validation based upon 

frequent zero, span and precision checks as well as a 

rigorous quality assurance program. 

A major consideration of this study was the establishment and utilization of a complete, regionally 

representative 0 3 database (unencumbered by missing data or local-scale variability) that would prove 
Spatiotemporal variability of non-urban 0 3 concentrations 2647 

most useful in assessing the adverse effects of nonurban 03 on both human health and vegetation. 

Attainment of this goal was achieved by employing 

numerous strict data selection criteria. First, we utilized the daily 1-h maximum concentration because 

both the primary standard, designed to protect human 

health and the secondary standard, designed to protect human welfare (i.e. vegetation, visibility, materials) established by EPA as part of the NAAQS are 

based on this measurement. Additionally, Lefohn et al. 

(1990) found that when assessing the adverse effects of 

03 on human health and vegetation, concentrations 

found in the upper end of the distribution curve are of 

greatest concern. Utilization of the daily 1-h maximum concentration also helps minimize local-scale 

variability because as Decker et al. (1976) have shown, 

at the time of maximum surface concentration (typically between 1 and 3 p.m. LST), the boundary layer is 

generally uniformly mixed and the surface concentration is therefore representative of the mean boundary 

layer concentration (Vukovich and Fishman, 1986). 

Second, to minimize chemical scavenging by NO 

found in urban areas, only those stations classified as 

either rural or suburban and reporting a land use of 

either forest, agriculture or residential were employed 

in the analysis. Rural stations received highest priority; however, to meet our third criterion, spatial 

completeness, several suburban stations were also 

included in the data base. (Results from the analysis 

indicate that the suburban stations selected in this 

study were indeed spatially representative and exhibited no more local-scale variability than the rural 

stations). And finally, only those stations reporting a 

capture rate of: (1) 90.0% or better for the entire study 

period, and (2) 80.0% or better for any one year, were 

included in the analysis. 

These selection criteria resulted in the inclusion of 

77 stations across the eastern half of the United States, 

the majority of which (55), are classified as rural 

(Fig. 1). Additional information concerning attributes 

• Rural Site 

• Suburban Site 

Fig. 1. Aerometric information retrieval system (AIRS) 

sites used in the analysis. 

of the stations, which are all located at low elevations, 

is provided in the Appendix. Several combinations of 

station "seasons" (season is defined as the period from 

1 April to 31 October) were examined before the 

optimum period of 1985-1990 was selected. This sixyear period was optimum in that it provided the 

maximum number of observations (98,868) and a total 

capture rate of 95.7%, which translates into an average of 205 of 214 days per station "season". All missing 

data were replaced using a linear interpolation 

scheme, across time. 

Although RPCA can be applied to non-normally 

distributed data (Rummel, 1970), the resulting correlation matrix (covariance matrix) used in the analysis 

may be poorly defined when, for example, the data are 

extremely skewed such that a very few extreme values 

dominate the resulting correlation coefficients. Examination of the probability distribution for the 03 data 

used in this analysis revealed a slight, positively skewed log-normal distribution (skewness=0.82). To 

determine if this non-normality would contaminate 

the PCA, both raw 03 data and O3 data subjected to a 

loglo transformation, as suggested by Holland and 

Fitz-Simmons (1982) were used in the analysis. Results 

of the analyses revealed insignificant differences between the PCA performed on the raw data and the 

logxo transformed data; subsequently the analysis 

used the rave data. 

3. PRINCIPAL COMPONENT ANALYSIS 

One of the main objectives of PCA is to identify, 

through a reduction of data, the recurring and independent modes of variation (signals) within a large, 

noisy data set, thereby summarizing the essential 

information of that data set so that meaningful and 

descriptive conclusions can be made. This is achieved 

through the sorting of initially correlated data into a 

hierarchy of statistically independent modes of variations which explain successively less and less of the 

total variance. For the sake of brevity, and because 

numerous pertinent references are available (i.e. Eder 

et al., 1987; Richman and Lamb, 1985) computational 

considerations are kept to a minimum in this paper. 

3.1. Methodology 

Mathematically, this analysis began with the extraction of a square, symmetrical correlation matrix R 

(having dimensions of 77 × 77), from the original data 

matrix having dimensions of 77 (stations)× 1284 

(days) and containing 98,868 observations. Selection 

of a correlation matrix (as opposed to a covariance 

matrix) has two advantages. First, use of a correlation 

matrix is much more suitable for resolving spatial 

oscillations (Overland and Preisendorfer, 1982), which 

is a major goal of the analysis; secondly, use of a 

correlation matrix allows isopleths of component 

loadings to be drawn, which can be regarded as the 

correlation coefficient between the component and the 
2648 B.K. EDER et aL 

individual station. By using R and the identity matrix 

(I), of the same dimensions, 77 eigenvector-eigenvalue 

pairs were derived. The eigenvectors represent the 

mutually orthogonal linear combinations or modes of 

variation of the matrix, while their respectiveeigenvalues represent the amount of variance explained by each of the eigenv¢ctors. By retaining only 

the first few eigenvector-eigenvalue pairs, or principal 

components, a substantial amount of the total variance can be explained while ignoring the higher order 

principal components which explain minimal 

amounts of the variance and can therefore be viewed 

as noise. 

When the elements of each eigenvector are multiplied by the square root of the associated eigenvalue, 

one obtains the principal component loading, which 

represents the correlation between the component and 

the station. (The square of a component loading is 

called the communality and indicates the proportion of 

variance at the individual station that can be attributed to that component.) When these principal 

component loadings are spatially mapped onto their 

respective stations for each component, isopleths of 

component loadings can be drawn. Examination of 

the temporal characteristics of the principal components can be accomplished with principal component 

scores. These scores, which are standardized, are 

weighted summed values whose magnitudes are dependent upon the daily 1-h maximum 0 3 concentration and the component loading. 

3.2. Number of components to retain 

The exact number of components that should be 

retained remains a subject of some controversy and 

uncertainty. While numerous techniques are available 

in the literature, no one method is considered fully 

satisfactory. Retaining too few components results in 

the blending of spatial patterns which are actually 

more discrete; whereas retaining too many components will delineate areas which represent nothing more 

than randomly generated variations (Richman, 1981). 

Application of the Scree test (Cattell, 1966), which 

entails the plotting of the percentage variance explained by each component in the order extracted and 

then looking for a discontinuity in the curve, suggested 

a six principal component solution. One criticism of 

this test is its subjectivity; subsequently, a completely 

objective significance test, the N rule method, developed by Overland and Preisendoffer (1982), was 

also applied to this data set. Using a random Monte 

Carlo simulation, it is based upon the null hypothesis 

that no spatial correlation exists between any two 

stations and that the 0 3 concentration for any given 

station is temporally independent. Unfortunately, results of Overland and Preisendorfer's simulation are 

presented for only a select number of variableobservation-component combinations, therefore interpolation was necessary. When applied to this data set, 

the N rule method indicated that the first six principal 

components should definitely be retained, with the 

seventh component considered marginal (due to the 

uncertainty of interpolation), and that the higher 

order principal components are considered noise. 

Comparisons were then made between the six and 

seven component solutions. These indicated that the 

seventh component simply "splintered" the first component and that no station recorded its highest loading (had the maximum amount of variance explained) 

with this seventh component; subsequently, the six 

component solution was selected. The fraction of total 

variance explained by these six components is presented in Table 1. 

The original data set, which contained 77 intercorrelated and noisy variables (stations) has therefore 

been reduced to one that contains only six orthogonal 

and thereby independent variables (principal components) and yet still explains almost two-thirds of the 

total variance. These results are very promising, especially given the highly variable nature of 03 and the 

fact that daily, non-smoothed data were used. 

3.3. Unrotated solution 

The first principal component (Fig. 2a, b), which 

accounts for 35.86% of the total variance, is the 

dominant component, that is the general tendency for 

the entire region to experience either high or low 03 

concentrations. Examination of Fig. 2a reveals, with 

the exception of southern Florida, an in-phase, oscillation (component loadings are all of one sign), which 

supports the hypothesis that 0 3 has a strong regionalscale component. The areas that load highest with this 

first component (northeastern, mid-Atlantic and 

Great Lakes states) tend to exhibit a strong and 

similar seasonality. As seen in Fig. 2b, the highest 

principal component scores (corresponding to the 

highest Oa concentrations for stations with positive 

loadings) tend to occur during the period from May to 

August. Scores associated with the months of April 

and especially September and October are consistently negative (associated with low 0 3 concentrations for 

stations with positive loadings). This nearly regionwide, seasonal-scale cycle is no doubt responding 

Table 1. Statistics for the first six unrotated principal components 

PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 

Eigenvalue 27.61 7.44 5.51 3.52 2.86 2.36 

Variance explained (%) 35.86 9.66 7.15 4.58 3.71 3.06 

Cumulative variance (%) 35.86 45.52 52.67 57.25 60.96 64.02 
Spatiotemporal variability of non-urban Oa concentrations 2649 

6O 

7O 

65 

5 

49 40 

PRINCIPAL COMPONENT 

ONE (35.86%) 

(a) 

I 

li 

oi 

-1i 

-2 

-3i 

_4 i 

_5i (b) 

i ...... i ...... 

1~5 

...... i ...... 

1987 1968 1900 

Fig. 2. (a) Component Ioadings and (b) daily time series (for 1 April-31 

October) of the standardized component scores associated with the first 

non-rotated principal component. 

primarily to the constant annual cycle of the region's 

climatology (i.e. solar insolation, temperature, cloud 

cover, etc.). 

Superimposed on and either enhancing or diminishing this dominant seasonal-scale cycle are more 

variable cycles, likely attributable to unique meteorological conditions found in various sections of the 

study region. For instance, the smaller loadings found 

in the southwestern and Gulf Coast states are indicative of a weaker seasonality. The negative loadings 

found in southern Florida indicate this area experiences a seasonality that is out-of-phase with the 

remainder of the study area. Here, 03 concentrations 

tend to be highest when the principal component 

scores are negative (i.e. April, September and October). Other features of this first component include the 

dominance of 1988 in the number of PC scores above 3 

standard deviation above the normal, and the lack of 

PC scores over 1.5 S.D. during 1989. 

The five remaining components (not shown) indicate more complicated, out-of-phase oscillations 

between various regions of the study area. Physical 

interpretation of these higher order non-rotated principal components is beyond the current scope of this 

analysis and as noted by Buell (1975), should be 

attempted with some degree of caution, because the 

shape of the boundary area to which the correlation 

coefficients belong can be a factor in determining the 

morphology of the non-rotated components. Because 

of this, non-rotated PCA is used mainly for data 

reduction and can be somewhat misleading when used 

for spatial delineation. 

3.4. Rotated solution 

Since one of the major goals of this paper was to 

delineate areas of homogeneous 03 concentrations, a 

rotation was performed on the components in order to 

better segregate the areas that have similar concentration characteristics. Of the many types of rotation 

possible, an orthogonal method developed by Kaiser 

(1958) was selected, because it rigidly rotates the 

predetermined principal components while retaining 

the constraints that the individual components remain 

orthogonal or uncorrelated to each other. This 

method increases the segregation between component 
2650 B.K. EDER et aL 

loadings, which in turn better defines a distinct grouping or clustering of intercorrelated data, thereby making spatial interpretation easier (Horel, 1981). Comparison between the statistics associated with the six 

rotated components (Table 2) and six unrotated components (Table 1), reveals that the total variance explained remains the same, 64.02%, but the distribution of the variance explained over the individual 

components is more uniform. 

The rotated solution allows us to regionalize or 

divide the study area into six separate, contiguous 

subregions, which each exhibit unique 03 concentration characteristics. As depicted in Fig. 3, loadings 

associated with the first rotated component define an 

ROTATED PRINCIPAL ~ ROTATED PRINCIPAL 

COMPONENT ONE ~ ~ COMPONENT TWO 80 f'~ 

"60 

5 6 

." b ~ Component Loadlngs (xlO0) 

ROTATED PRINCIPAL /~ ROTATED PRINCIPAL (~ 

COMPONENT FOUR ~ ~ op~ 

~_~ Component Load~gs (xlO0) 

R Oo~ D P R~NFC/PEAL _ f~ OC, R o~AT~ D P:/NsI C '; AL _ 

Fig. 3. Component Ioadings associated with the six rotated principal components. 
Spatiotemporal variability of non-urban 03 concentrations 2651 

Table 2. Statistics for the first six rotated principal components 

PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 

Eigenvalue 13.55 10.82 8.58 7.58 5.46 3.30 

Variance explained (%) 17.60 14.05 11.14 9.84 7.09 4.30 

Cumulative variance (%) 17.60 31.65 42.79 52.63 59.72 64.02 

area encompassing Great Lakes region north and west 

of the Ohio River. The second component encompasses the northeast States from Pennsylvania and 

northern New Jersey, northward. The highest loadings associated with the third rotated component are 

found over the south, while the fourth component 

defines the mid-Atlantic region from North Carolina 

to Maryland. The fifth component defines the southwest part of the study area from Texas and Louisiana 

northward to Kansas, and finally loadings associated 

with the sixth component highlight stations in Florida. 

All six maps can be incorporated into one by plotting 

the maximum loadings for each station on their 

respective rotated principal component (Fig. 4). The 

communalities for each station, which again indicate 

the amount of variance explained by the component, 

are provided in the Appendix. 

One of the most apparent features of Figs 3 and 4 is 

the regional nature and strength of all six rotated 

components. Each component identifies an "influence 

regime" that distinguishes a different and well segregated area of the study domain. The subregions, as 

delineated by the 0.40 component loading isopleth are 

reasonably uniform in size, and exhibit little overlap. 

Of the 77 stations, only 7 (9.1%) recorded loadings of 

0.50 or greater (had more than 25% of the variance 

explained) on two subregions. This lack of overlap 

illustrates the sharpness of the station segregation and 

the strength of the subregion formation. The few 

stations that did load highly on more than one 

subregion were, without exception, located on the 

border between subregions. This suggests that the 

boundaries separating these subregions are slightly 

more fuzzy, and, as will be explored below, may 

indicate the presence of some cross-boundary correlation. Only one station, Johnson City, TN (471632002), 

failed to record a loading of at least 0.40 with any 

subregion. This lack of association may be attributable to this site's elevation (at 487 m this is the highest 

of all sites), and the unique 03 characteristics often 

observed at higher elevation sites (Aneja and Li, 1992). 

In order to determine the stability of this rotated 

principal component solution (i.e. stability of the six 

homogeneous subregions), several tests were performed. First, several subsets (in both time and space) 

of the original data matrix were used in the analysis. 

Neither variations in the length of the 03 season (i.e. 

April-October vs May-September) nor variations in 

the stations resulted in any marked differences from 

the original solution. Secondly, to determine if any 

cross-subregion correlation existed, the orthogonality 

constraint was relaxed when rotating the components. 

6 

LANT/C' 

Maximum Component Loadings (xlO0) 

Fig. 4. Six homogeneous Oa concentration regions as depicted by the maximum component loadings. 

This "relaxation" was achieved through an application of the Harris-Kaiser case II Oblique rotation 

(Harris and Kaiser, 1964), which has the same general 

objective as a Varimax rotation (maximization of the 

variance of the component loadings between components for a given station), but it entirely relaxes the 

orthogonality constraint (Richman and Lamb, 1985). 

An obliquely rotated solution will only resemble an 

orthogonally rotated solution if the correlation exhibited between subregions is small. For this analysis, the 

oblique solution was very similar to the orthogonal 

solution (only 4 of the 77 stations transferred subregions) and with only a few exceptions (found between several adjacent subregions) cross-correlations 

were quite weak, ranging mostly from -0.20 to 0.20. 

This supports the application of the orthogonal rotation and suggests that the physical factors determining 

the uniqueness of 03 concentrations for each of these 

subregions are indeed different. 

4. TIME SERIES ANALYSIS 

Having delineated six homogeneous O3 concentration patterns, elucidation of the temporal variance of 

each of these subregions can now be performed 

through an examination of the rotated principal component scores, which are weighted summed values 

whose magnitudes are dependent upon the daily O3 

concentration and the principal component loading. 

The component scores are standardized, so they have 

AE(A) ZT:Zr-N 
2652 B.K. EVER et al. 

a mean of zero and standard deviation of one. Positive 

scores correspond to higher than normal 03 concentrations, while negative scores represent lower than 

normal concentrations. 

4.1. Daily principal component scores 

When plotted as a time series, the daily principal 

component scores provide excellent insight into the 

6 

4. 

3" 

1. 

i °" 

--3' 

--4" 

i ...... I .... 

a 

I --1' 0 

i ...... J ..... 

11 

0! 

-1 ! 

-2i 

-4 

-5 

...... i ...... 

e 

O:EAT tail8 

...... i ............ i ..... 

spectrum of temporal variance experienced by each 

subregion (Fig. 5). Such information should prove 

useful to scientists who employ Oa simulation models 

like the Regional Oxidant Model (Young et al., 1989) 

or the Urban Airshed Model (Tang et al., 1990). These 

models are used to simulate potential reductions in 03 

concentrations resulting from various emission control strategies. Unfortunately, it is cost prohibitive to 

..... i ............ i ..... 

i 

.. .... r ...... i 

1~0 

~ SIJI~-Cd¢~ 

...... i ............ r ..... ..... i ............ ) .......... i ...... i 

~IJTH SOElflEP.~ 

..... i ............ j ............ i ..... 

19~ ~7 W 

..... i ............ i ...... i 

Fig. 5 (a)-(c). 
Spatiotemporal variability of non-urban 03 concentrations 2653 

MID -A~ $1,,u"~ilON 

5- 4i 3! I 2i 

li 

ol 

-Ii 

-2 ~ 

~3" 

-4i 

-5 i 

...... i ...... 

1906 

...... i .... ...... i ............ i ............ i ...... 

1907 1908 W 

YEAR 

~XJTttWEST SLX~-G~ 

...... i ...... i 

1~0 

5" 

4- 

3" 

1- 

O 

~2' 

~3 ~ 

~4' 

i .......... ...... r ............ J ...... 

1~6 1907 19o8 w 

RORDA SUOFEGR~ 

...... i ...... 

1~0 

5 

4 

i ...... i ............ i ............ J ...... 

f 1~6 1906 1987 

...... i ...... 

1908 

...... i .......... i ...... 

1~0 1~0 

Fig. 5. Daily time series of the standardized principal component scores associated with 

the six homogeneous subregions. 

simulate each day; subsequently, modeling is confined 

to selected episodes during which the highest 03 

concentrations are observed. Our results will allow 

modelers, upon quick examination of the time series 

associated with each subregion, to determine the 

optimum period for performing model simulations. 

For example, modelers wanting to determine the 

optimum simulation period for the northeast states 

could simply examine the daily time series associated 

with the northeast subregion. The series reveals that in 
2654 B.K. EDER et al. 

1988 a total of 10 days were observed with principal 

component scores greater than 3.0 standard deviations above the six year normal. No other year for 

this subregion recorded more than one score greater 

than 3.0. Additionally, eight of these days occurred 

within a 31-day period, from 16 June to 16 July, 

signifying the most intense episode experienced by the 

subregion, and subsequently the optimum simulation 

period. 

4.2. Seasonal trends 

Though somewhat noisy, the time series associated 

with the six subregions reveal numerous features, 

including the presence of seasonal cycles. In order to 

~:IEAT LAKES SUBFtE~ON 

I ,h I, I,, 

MAY dUN ~ AUG SEP OCt 

MONTH 

NORTHEAST SI,,IBI:IEC-,ION 

o:I 

, .............. , .............. , .............. , .............. i .............. ) .............. , .............. ) .............. i .............. , .............. , .............. i .............. , .............. , .............. ( 

.aOFl MAY dUN JUL AUG SEP OG'q" b 

MONTH 

SUI~FIE~ON 

i] .I II. IL,, ~,I d 

c ~ MAY ,RJN ,RJI. ~ SEP OCT 

Fig. 6 (a)-(c). 
Spatiotemporal variability of non-urban 03 concentrations 2655 

MID-ATLANTIC SUBREGION 

2 o 

I --1" °" 

-2- 

Al~ MAY ~ ~ AUG ~ OCT d 

MONTH 

soun-lV~S'T SUBREGION 

2" 

0 ~ 

~1 ~ 

-2 

AI~ MAY ~ ,JUL AUG 8EP 

MONTH 

FLORIDA SUBREGION 

2 ~ 

I 1 

0 

-1 

-2 

MAY JUN JUL AUG SEP OCT 

MON~ 

Fig. 6. Seasonal time series of the standardized principal component scores associated 

with the six homogeneous subregions as defined by the median scores for the six year 

period. A cubic spline function was used to smooth the data (thick line). 

better characterize these seasonalities, which generally 

exhibit interannual consistency, and to determine 

their relative strengths, "typical" seasons were constructed for each subregion by calculating the median 

score for each day across the six year period. The 

median was selected as opposed to the mean to 

prevent the dominance of any one year in determining 

the typical season. These median scores were then 

smoothed utilizing a cubic spline smoother (Reinsch, 

1967), revealing that each subregion displays a unique 
2656 B.K. EDER ef at. 

03 climatology, with variations in the strength, symmetry and timing of the seasonal-scale cycles (Fig. 6). 

To facilitate comparison between each subregion's O3 

climatology, all six smoothed seasonal curves are 

presented in Fig. 7. 

Quantification of the relative strengths of these 

seasonal-scale cycles was achieved by calculating the 

percentage of total variance associated with the cubic 

spline curve. The remaining variance, associated with 

the residuals (found by subtracting the seasonal-scale 

cycle from the raw scores) can be attributable to 

synoptic-seale variability. When summed, these two 

components of variance equal one because the raw 

scores are standardized and by definition have a mean 

of zero and a variance of one (Table 3). 

The Great Lakes, northeast, south and mid-Atlantic 

subregions all exhibit a seasonality generally characterized by higher values during the months of 

May-August. (This supports the results of the first 

non-rotated principal component). There are, however, interesting differences. The Great Lakes (seasonal-scale variance (ssv)=0.20 or 20% of the total 

variance) and mid-Atlantic (ssv=15%) subregions 

tend to observe a stronger seasonal cycle with positive 

scores being symmetrical around the last week in June 

and the first week of July, respectively. The strength 

and symmetry of this seasonality is weakened and its 

timing delayed for both the northeast (ssv = 12%) and 

south (ssv = 7%) subregions. Maximum scores for the 

northeast subregion tend to occur during the last week 

of July, while they are delayed until the first week of 

August for the south subregion. The southwest subregion (ssv=4%) experiences a greatly diminished 

seasonality, with the timing of maximum scores delayed until the middle of August. The seasonality 

found in the Florida subregion (ssv= 33%) is unique 

in both its timing and strength. The highest scores 

occur consistently during the months of April and 

May, with the remainder of the season experiencing 

negative scores. 

4.3. Persistence 

Having characterized and quantified the seasonalscale structure, the time series were then deseasonalized and analysed for other non-random 

variability. Deseasonalization was achieved by subtracting each subregion's smoothed median scores 

LU 

nO 

0 

I.u 

Z 

=o 

ro 

1.25 

1.00- 

0.75 - 

0.50- 

0.25 - 

0.00 

-0.25- 

-0.50- 

-0.75 - 

-1.00 - 

-1.25 

..................... I ~d~EAT I.AI~ .1~....E~..~. I~ "~'¢~ - 

................... 

' I ' I ' I ' I ' I ' I ' I APR MAY JUN JUL AUG SEP OCT 

MONTH 

Fig. 7. Composite seasonal time series as defined by 

smoothed median principal component scores for each of 

the six homogeneous subregions. 

from each of their six years of raw scores. Autocorrelations (to a lag of 20 days) were then calculated for each 

year, in order to determine the amount (if any) of 

persistence that occurs within each subregion. The 

median autocorrelations (of the six years) were then 

determined for each lag and subregion and are plotted 

in the form of a correlogram (Fig. 8). 

Examination of the correlograms reveals that 03 

concentrations tend to be much more persistent in the 

south, southwest and Florida subregions as autocorrelations are significant (95% confidence level) to lags 

of 3, 3 and 4 days, respectively. Lag1 correlations were 

also quite high and ranged from 0.61 for the south, 

0.64 for the Florida, and 0.70 for the southwest 

subregions. In these subregions, once an anomaly 

from the seasonal cycle is established, whether it be 

positive or negative (corresponding to either high or 

low 03 concentrations), there is a tendency for it to 

persist for days. This characteristic was strongly supported through an examination of the deseasonalized 

(or residual) principal component scores (not shown). 

Conversely, correlograms associated with the remaining three subregions indicate that 03 concentrations 

tend to be less persistent, as autocorrelations are 

significant only to a lag of 2 clays for the Great Lakes 

and mid-Atlantic suhregions and to a lag of 1 day for 

the northeast subregion. Lag I correlations were also 

lower, ranging from 0.56 in the Great Lakes subregion, 

Table 3. Segregation of total subregion variance into seasonal- and synoptic-scale components 

Variance 

Subregion Total (raw scores) Seasonal-scale (cubic spline) Synoptic-scale (residuals) 

Great Lakes 1.00 0.20 0.80 

Northeast 1.00 0.12 0.88 

South 1.00 0.07 0.93 

Mid-Atlantic 1.00 0.15 0.85 

Southwest 1.00 0.04 0.96 

Florida 1.00 0.33 0.67 
Spatiotemporal variability of non-urban O a concentrations 2657 

0.8 

0.7 

0.6 

_~0.5 

~ 0.4 

0.3 

~ 0.2 

00.1 

0.0 

-0.1 

-0.2 

GREAT LAKES SUBREGION 

i 

1 2 3 4 5 6 7 8 9 1011121314151617181920 

LAG 

0.8 

0.7 : 

0.6 - 

0.5 

0.4 

0.3 

0.22 

O0 0.1: 

 .11 4).2 

b I 

NORTHEAST SUBREGION 

2 3 4 5 6 7 8 9 1011 121314151617181920 

LAG 

1 

C 

SOUTH SUBREGION 

2 3 4 5 6 7 8 91011121314151617181920 

LAG 

z 

0 

uu 

8 

MID-ATLANTIC SUBREGION 

1 2 3 4 5 6 7 8 9 1011121314151617181920 

d LAG 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

~ 0.2 

~ 0.1 

0.0 

-0.1 

-0.2 

e 

SOUTHWEST SUBREGION 

1 2 3 4 5 6 7 8 9 1011121314151617181920 

LAG 

0.8 

0.7 

0.6 

0.5 

~ 0.4 

0.3 

~ 0.2 

~ O.t 

0.0 

41.1 

-0.2 

1 f 

FLORIDA SUBREGION 

2 3 4 5 6 7 8 91011121314151617181920 

LAG 

Fig. 8. Correlograms of the deseasonalized daily standardized principal component scores associated with the six 

homogeneous subregions. 

to 0.52 in the mid-Atlantic and 0.47 in the northeast 

subregions. These findings suggest that anomalies 

around the seasonal cycle tend to oscillate much more 

frequently (again supported by the residual scores) in 

these subregions, such that extended periods of high or 

low scores (corresponding to high or low 0 3 concentrations) are less common. 

Spectral Density Analysis (SDA) using the finite 

Fourier transformation (Box and Jenkins, 1970) was 

also applied to each of the deseasonalized time series 

associated with the six subregions. The analysis decomposes the time series into a sum of sine and cosine 

waves of varying amplitudes and wavelengths, yielding a measure of the distribution of the variance over a 

continuous spectrum of all possible wavelengths. As 

with the correiograms, the SDA was performed individually, on each of the six year's residuals, with the 

median spectral estimates then being computed for 

each frequency (Fig. 9). In those subregions which 

experienced greater persistence (south, southwest and 

Florida), the spectral density analyses display classic 

Markov "red noise" characteristics (also indicative of 

persistence), where the spectral estimates decay exponentially with increasing frequency (WMO, 1966). 

Conversely, the spectral estimates for the remaining 

subregions (Great Lakes, northeast and mid-Atlantic) 

display "white noise" characteristics (indicative of 

purely random features), where the spectral estimates 

are rather uniform across frequency. 

5. OZONE CHARACTERIZATION 

Having regionalized the eastern United States of 

America into "influence regimes", or areas that exhibit 

homogeneous 03 characteristics, further elucidation 
2658 B.K. EDER et al. 

and comparison of these unique characteristics will 

now be achieved through a variety of simple analyses 

involving the raw O3 data found within each subregion. Statistics in this section, which are compiled 

over all stations found within the six individual subregions (see the Appendix for station appropriation), 

provide useful regionwide indicators of O a distributions and trends. 

5.1. Summary statistics 

Summary statistics for each subregion and the 

domain as a whole were calculated and are presented 

in tabulated form (Table 4) and as boxplots (Fig. 10). 

The boxplots include 1st and 99th percentiles (open 

circles), 10th and 90th percentiles (bars), 25th and 75th 

percentiles (rectangle ends) and the median (center 

1 0.7 

~ 

0.6 

0.5 

0 

a 

° 0.7 I 

~ 

0.6 

0.5 

0 

i 

o 

6 12 18 24 

6 12 18 

6 12 18 

~qEAT LAKES St~:EGION 

3036424854606672 

NOFm-~ST SUBREGION 

78 84 

24 30 364248546O6672 78 84 

24 30 

Fig. 9 (a)-(c). 

78 84 

90 

90 g6 

g0 gO 

1(12108 

102108 

1~10e 
0.7 

~ 

0.6 

0.5 

Spatiotemporal variability of non-urban 0 3 concentrations 

MID-A~ SUBREGION 

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 

FIC-QUB¢~ (~ Days) 

SO~'HWEST SUBREGION 

66 1(~ lOe 

0 e '~2 le 24 30 ~ 42 48 54 eo 66 72 7e e4 

FLORIDA ,~BREGI~ 

g0 66 10g 1~ 

J 

0.8- 

0.7- 

0.0. 

0 6 12 18 24 30 36 42 48 54 eo ~ 72 

f mEatJe~cv ~ D~s) 

70 84 66 66 1~ 100 

Fig. 9. Spectral density analysis of the deseasonalized daily standardized principal 

component scores associated with the six homogeneous subregions. 

2659 

line) and mean (solid circle). As discussed in the data 

section, Table 3 reveals that the distribution of the 

entire data is slightly positively skewed (skewness 

=0.82) and slightly leptokurtic (kurtosis= 1.64), or 

more peaked than a normal distribution. Across subregions, the skewness ranges from 0.44 (south) to 1.08 

(northeast) and 1.11 (southwest). This indicates that 

the southwest and northeast subregions would tend to 

experience more exceedingly high 03 concentrations 

(relative to their respective mean concentrations), 

while the south would tend to experience less high 

concentrations. The kurtosis values ranged from 0.31 
2660 B.K. EDER et al. 

Table 4. Summary statistics of the daily 1-h maximum 03 (ppb) concentrations associated with each subregion (and their 

rank from highest (1) to lowest (6)) and the entire domain 

Subregion N Mean C.V. (%) Skewness Kurtosis % >80 % > 120 

Great Lakes 26,964 53.83 37.48 0.81 1.95 9.7 0.6 

(5) (4) (3) (3) (5) (5) 

Northeast 19,260 54.61 40.93 1.08 1.97 13.1 1.3 

(4) (1) (2) (2) (4) (3) 

South 17,976 58.64 37.04 0.44 0.84 15.0 0.9 

(2) (5) (6) (5) (2) (4) 

Mid-Atlantic 14,124 63.23 35.97 0.70 1.16 20.3 1.9 

(1) (6) (4) (4) (1) (1) 

Southwest 14,124 55.15 40.60 1.11 2.98 14.0 1.5 

(3) (2) (1) (1) (3) (2) 

Florida 6420 48.74 40.17 0.63 0.31 7.1 0.2 

(6) (3) (5) (6) (6) (6) 

Domain 98,868 56.06 39.02 0.82 1.64 13.9 1.1 

140 

120 

100 

8O 

uJ 

Z 6O 

O 

N 

O 

40 

2O 

0 ~ T 

G'Lakes N'east 

o 

o 

I I ~ i i 

South M'Atlantic S~vest Florida Domain 

SUBREGION 

140 

120 

100 

80 

60 

40 

20 

0 

Fig. 10. Boxplots of the O a concentrations associated with 

the six homogeneous subregions and the entire domain. 

(Florida) to 2.98 (southwest), indicating that the distribution of 03 concentrations found in the Florida 

subregion is more normal, while that found in the 

southwest subregion is leptokurtic. Variance, as defined by the coefficient of variation, is highest and 

nearly equal in the northeast (40.9%), southwest 

(40.6%), and Florida (40.2%) subregions, and lowest 

in the Great Lakes (37.5%), south (37.0%) and midAtlantic (36.0%) subregions. 

Ozone concentrations are considerably higher, 

across all percentiles, in the mid-Atlantic subregion. 

Accordingly, the percentage of days that concentrations exceed 80 ppb (20.3%) and 120 ppb (1.9%) are 

also the greatest in this subregion. Translated into a 

typical season, 03 concentrations in this homogeneous region exceed 120 ppb (the NAAQS) 4 times, 

and they exceed 80 ppb (a threshold above which 0 3 

contributes to forest decline (McLaughin, 1985)) more 

than 43 times. The south subregion experiences the 

next highest concentrations, across all percentiles except the 99th (which ranks 4th highest out of six 

subregions). This is consistent with the lower skewness 

(0.44) exhibited by the distribution of this subregion, 

and indicates that while mean concentrations tend to 

be higher, extremely high concentrations are less 

common. This is also reflected in the ranking of the 

percentage of days exceeding 80 ppb (15.0%--ranked 

2nd) and 120 ppb (0.9%--ranked 4th). 

The distributions of concentrations for the southwest, northeast and Great Lakes subregions are somewhat more similar, especially for the measures of 

central tendency; however, concentrations for the 

southwest and northeast subregions are more positively skewed, subsequently, their 99th percentiles rank 

second and third, respectively. This is again reflected 

in the number of days exceeding 80 ppb (14.0% for the 

southwest--ranked 3rd, and 13.1% for the northeast 

--ranked 4th) and 120 ppb (1.5% for the southwest-- 

ranked 2nd and 1.3 % for the northeast--ranked 3rd). 

This indicates that while concentrations are typically 

near the domain average in these two subregions, their 

positively skewed distributions allow for more extremely high values. Ozone concentrations found in 

the Florida subregion exhibit more normal tendencies 

and are also substantially lower across all percentiles, 

than the other subregions. 

5.2. Monthly distributions 

Monthly 03 distribution statistics associated with 

each subregion were also calculated and are presented 

(Fig. 11) in the form of boxplots, Analysis of the actual 

03 concentrations within each subregion replicates 

the time series analysis of the principal component 

scores in that a strong seasonality, with maximum 

concentrations generally centered around the period 

from June to August is observed in the Great Lakes, 

northeast, mid-Atlantic and south subregions. 

Statistics for the Great Lakes subregion reveal that 

concentrations are equally highest in June (mean: 

65ppb, 75th%: 75.1ppb, 90th%: 91.0ppb, and 

99th%: 125.1 ppb) and July (63.4, 74.9, 89.7 and 

124.0 ppb). The northeast subregion experiences its 

highest concentrations during July (67.2, 82.4, 102.3 

and 144.7 ppb), as does the mid-Atlantic subregion 

(77.1, 91.9, 111.2 and 130.0ppb). In the south sub- 
Spatiotemporal variability of non-urban O 3 concentrations 2661 

0 

140- 

1~0: 

tO0: 

140- 

120 

I00 ~ 

g= 

20 

GREAT I..~ ~JBRE~ 

o 

o 

o 

o 

o 

APR MAY JUN JUt. ~ ~ OC'T 

MONTH 

160 :1 o 

40t 

20~ o o 

0 ~ 

MAY 

h 

NORn-EAST SUBREGaON 

O 

O O 

o o 

o 

dUN dUL AUG SEP 

SOUTH SUl3REG~N 

o o o 

o 

o 

o o 

APR MAY JUN ~l. AUG SEP 

MONTH 

o 

0 l 

OCT 

d 

OCT 

soun-tv~sT ~J~EC~ON 

o o o o 

o 

o 

o 

MID - A'n..,N~IC SUBREGION 

o 

o 

o 

o 

o o 

o o o 

o 

o o 

o 

APR MAY ~ dU. AUG ~ OCT 

MONTH 

MAY JUN JUL AUG ~ ~ ~ MAY JUN JUL ~ S~ ~ 

MONTH f MONn~ 

Fig. 11. Boxplots of the monthly 0 3 concentrations associated with the six homogeneous subregions. 

:1o o o o o o 

 Ioo .... o 

0 

region, high concentrations are found equally during 

June (66.2, 80.2, 95.9 and 130.0 ppb), July (65.8, 80.0, 

94.9 and 129.2 ppb) and August (65.1, 79.9, 92.9 and 

127.4 pph). Also supporting the earlier time series 

analysis is the lack of a strong seasonality in the 

southwest subregion. Here, mean concentrations remain between 55 and 60 ppb (with the exception of 

September and October when they are lower). The 

90th% does show a propensity for being slightly 

higher during June-August (90.0 ppb). The out-ofphase seasonality observed in the Florida subregion 

time series is also replicated, as actual monthly 03 

concentrations differ completely from the other subregions. Highest concentrations are found during the 

months of April (61.3, 69.9, 81.8 and 107.2 ppb) and 

May (57.5, 70.1, 81.4 and 105.6 ppb). Concentrations 

for the remaining months are considerably lower and 

exhibit little variation. 

5.3. Annual distributions and trends 

Having examined the monthly variability within 

subregions, we can now focus on the annual variability 

as seen in Fig. 12. Within each subregion, the years 

were ranked in descending order based upon the 

higher percentile statistics (from the mean and median 

to the 99th%). For the Great Lakes subregion, 1988 

was the worst year for high Oa concentrations, followed by 1987. The remaining years were more closely 
2662 B.K. EDER et al. 

140 

100. 

4O 

20 

0 

a 

140- 

IO0 ~ 

40 

20 

140  ̧ 

120 ~ 

100. 

40 

20 

0 

e 

(~.AT LAKES 8US~GI~ 

o o o 

o 

o o o o 

o 

o 

ms ms 1967 1908 m 

StJS~GION 

le0 

140 o 

o o 

o o • 

20 o 

o o o o 

o 

0 

h ms ms m7 ms ms 1~0 

YEAR 

soun-i SUBFEGION 

o 

° 

o 

o 

o 

o 

o o 

o o ° 

o 

MID-ATLANTIC SUBREGION 

1~0 

201 o o 

0 ~ 

d 

o 

o 

o o o o 

1~7 1~8 19~ 

YEAR 

o o o 

o o 

o o o ° 

ms ~ 1967 W ms 

YFJ~ 

~- o o o 

20 

o o o o o 

0 

f 

Fig. 12. Boxplots of the annual O s concentrations associated with the six homogeneous subregions. 

o 

o 

grouped and are, in descending order, 1989, 1985, 1986 

and 1990. The northeast subregion also experienced 

its highest concentrations during 1988; however, 1985 

(not 1987) recorded the second highest concentrations, 

followed closely by (again in descending order) 1987, 

1986, 1989 and 1990. For the south subregion, 1988 

was again the worst year. This was followed by 1990 

and 1987, in which the 99th % and 95th % were higher 

in 1990, but the 75th, mean and median were higher in 

1987. The remaining years in descending order were 

1986, 1985 and 1989. For the mid-Atlantic subregion, 

the worst year, 1988, was followed by 1987, then 1985, 

1986, 1990 and finally 1989. Ranking the years was a 

little more difficult for the southwest subregion. AIthough 1988 recorded the overall highest concentrations, it recorded the lowest 99th % of all six years. The 

remaining years in descending order were identical to 

the south subregion: 1987, 1985, 1990, 1986 and 1989. 

Ranking the years for Florida was also difficult, as no 

one year stood out in terms of high concentrations. 

The years of 1986, 1987 and 1988 were slightly higher 

than 1989 and 1990, while the lowest concentrations 

occurred during 1985. 

In order to determine if the yearly 03 concentrations associated with the six subregions exhibited any 

trends, a simple linear regression was performed on 

selected statistics (mean, median, 75th, 90th and 99th 

percentiles), and the estimated slopes (and their stand- 
Spatiotemporal variability of non-urban O3 concentrations 

Table 5. Estimated trend line slopes (ppb yr- ~) for selected statistics for each subregion and the entire 

domain. (Standard error of estimate provided in parenthesis) 

2663 

Subregion Mean Median 75th % 90th % 99th % 

Great Lakes 0.16(1.00) 0.23 (0.93) --0.09(1.36) 0.26(2.19) -0.23 (2.92) 

Northeast -0.57(0.67) -0.54 (0.29) -0.83(0.90) -0.51(2.55) -0.57 (3.55) 

South 0.19(0.92) 0.00 (0.73) 0.23(1.23) 0.77(1.88) 1.46 (2.46) 

Mid-Atlantic -0.87(0.64) -0.94*(0.32) -1.20(0.86) -0.69(2.49) -0.23 (3.54) 

Southwest -0.18(0.60) -0.06 (0.90) -0.14(0.81) -1.31(0.73) -0.46 (1.28) 

Florida 0.34(0.35) 0.69 (0.55) 0.06(0.47) -0.31(0.49) - 1.37t(0.54) 

Domain -0.16(0.66) -0.17 (0.62) -0.26(0.94) -0.20(1.83) 0.06 (2.51) 

* Significant at 95%. 

f Significant at 90%. 

ard errors) of the model calculated (Table 5). As with 

most studies examining trends in 03 concentrations 

(Lefohn and Shadwick, 1991, and Korsog and Wolff, 

1991, for examples), this analysis was hindered by a 

limited number of years. As a result, the standard 

errors associated with the estimates of slopes are large; 

consequently any trends in the data would have to be 

very pronounced before any definitive conclusions 

could be made. 

Although there is a slight decrease (across all statistics) in the northeast, mid-Atlantic and southwest 

subregions and a slight increase in the south subregion 

(across all statistics) and the Great Lakes and Florida 

(mean and median), only 2 of the 35 statistics exhibited 

significant trends (90% C.L.), which given the number 

of tests, could be attributable to chance. Therefore, we 

would conclude that over the six-year period 

1985-1990, 03 concentrations, as represented by our 

regionwide indicators, exhibited no statistically discernible trends. 

6. DISCUSSION 

Intuitively, any discussion concerning the probable 

underlying physical mechanisms responsible for the 

statistically unique Oa patterns should begin with an 

examination of possible causal relationships with both 

meteorological and emissions patterns. However, in a 

climatological study of non-urban 03, Vukovich and 

Fishman (1986) found that because 03 is a secondary 

pollutant (not directly emitted into the atmosphere), 

its spatial behavior in the rural boundary layer is not 

greatly affected by major source regions (i.e. not 

directly related to the distribution of its precursor 

emissions of NOx and VOCs.) Rather, the most important determining factor was the path and frequency of anticyclones, which because of requisite 

meteorological conditions (i.e. warm temperatures, 

clear skies and stagnating conditions), are conducive 

to the synthesis of 03 (Vukovich et al., 1977). Subsequently, our discussion, which will attempt to provide insight into the physical mechanisms shaping the 

spatial and temporal morphology of the subregions, 

will focus on stagnating conditions associated with 

anticyclones and will utilize climatological studies by 

Korshover (1975), Zishka and Smith (1980) and 

Korshover and AngeU (1987). 

An examination of Korshover's (1975) air stagnation climatology for the period 1936-1975 shows 

that on an annual basis, areas of the United States of 

America east of 90 ° W and south of 40 ° N are, by far, 

the most susceptible to stagnating conditions (greater 

than 100 stagnating days during his study), with the 

maximum stagnation occurring in Georgia (nearly 

500 stagnating days). Korshover attributed this susceptibility to the westward extension of the quasipermanent Bermuda High, which is normally located 

off the southeastern coast (Vukovich and Fishman, 

1986), but occasionally migrates westward, engulfing 

the southeast quadrant of the United States of America in stagnating conditions conducive to the formation of O3. Examination of Fig. 4 reveals that this 

general area corresponds quite well with our study's 

Florida, south and mid-Atlantic subregions, leading to 

the hypothesis that stagnating conditions associated 

with the westward extension of the Bermuda High 

largely control the 03 climatology of these three 

subregions. 

Differentiation between these three subregions may 

be attributable to seasonal differences in the timing, 

strength and location of the stagnating conditions. 

During the spring (March-May), maximum air stagnation conditions are usually found in northern Florida (Korshover, 1975; Korshover and Angell, 1987). 

Examination of the time series of both the standardized principal component scores (Figs 6 and 7) and 

the raw 03 data (Fig. 11) associated with the Florida 

subregion support this contention, as the highest 

concentrations are consistently observed during the 

first two months of the 0 3 season (April and May). 

During the summer (June-August), the area of maximum stagnating conditions moves northward (completely out of Florida), and extends into Georgia, 

North Carolina, Virginia, West Virginia and Maryland. This northward migration is well represented in 

the time series of the mid-Atlantic subregion (Figs 6, 7 

and 11) where maximum concentrations tend to occur 

during this three month period. Toward the end of 

summer and into early autumn, the area of maximum 
2664 B.K. EnER et al. 

stagnation shifts southwestward, resulting in an 

elongated maximum stretching from Mississippi back 

to North Carolina. This deviation is also well represented in the time series associated with the south 

subregion, where maximum 03 concentrations tend to 

occur later (during the period July-early September) 

than in the mid-Atlantic subregion. Although the 

southwest subregion is normally west of the stagnating conditions, this subregion's only seasonal perturbation (a slight increase found during August and 

early September--Figs 6, 7 and 11), may too be 

attributable to this late summer, early autumn southwest shift in stagnating conditions. 

The spectral density and correlogram analyses of 

these subregions generally support the hypothesis that 

stagnating conditions associated with the westward 

extension of the Bermuda High is responsible for the 

03 climatology in these subregions. Persistence is 

generally stronger in the south (lagt autocorrelation 

= 0.61 and autocorrelation significant to 3 days)and 

Florida (0.64, 4 days) subregions, which would correspond to the presence of a slow moving, or even 

stationary, stagnating anticyclone. It is worth noting 

that persistence is also large in the southwest subregion (0.70 and 3 days). Potential reasons for the 

weaker persistence (0.52, 2 days) found in the midAtlantic subregion are discussed below. 

The gradient of stagnating days in Korshover's 

study drops off rapidly north of 40 ° N. This area 

corresponds well with this study's northeast and Great 

Lakes subregions. It is hypothesized that the 03 

climatology in these two subregions is likely controlled by migratory anticyclones, which because of 

their proximity to the summer jet stream, are more 

transient in nature than the stagnating anticyclones 

associated with the westward extension of the 

Bermuda High. In a climatological study, Zishka and 

Smith (1980) found that these migratory anticyclones 

generally originate in western Canada and traverse the 

northern portion of the United States of America, 

generally along the 40th parallel. On the rare occasions when these migratory anticyclones do move 

south of 40 ° N, they generally undergo airmass modification and become amalgamated with the western 

extension of the Bermuda High. (This transformation 

often occurs in the mid-Atlantic subregion and may be 

the cause of the weaker persistence found here in 

comparison to the the south and Florida subregions.) 

Because of the more transitory nature of these 

anticyclones, one would expect that O3 concentrations would be less persistent. Examination of the 

spectral and correlogram analyses of these two subregions support this contention in that 03, concentrations tend to be considerably less persistent (lag1 

autocorrelation = 0.47 and autocorrelation significant 

to 1 day) in the northeast and Great Lakes (0.56, 2 

days) subregions. Physical differentiation between 

these two subregions is somewhat more tenuous, but 

may be attributable to findings by King and Vukovich 

(1982), who noted that O3 concentrations in these 

migratory anticyclones increase as the anticyclone 

moves east across the northern United States of 

America. This increase, which is in response to increased residence times (the time the air parcels remain 

under the influence of the anticyclone and its accompanying favorable meteorological conditions), is reflected in Fig. 10, where compared to the Great Lakes 

subregion, ozone concentrations in the northeast subregion tend to be somewhat higher. 

7. SUMMARY 

The spatial and temporal variability of non-urban 

O3 concentrations over the eastern United States of 

America during the period 1985-1990 was examined 

through the use of a multivariate statistical technique 

called Principal Component Analysis. The original 

data set, which contained 77 correlated variables 

(monitors) was reduced to six uncorrelated principal 

components, while still explaining almost two-thirds 

(64.02) of the total variance. Application of Kaiser's 

Varimax rotation led to the identification of six separate, contiguous subregions or "influence regimes" that 

each exhibit statistically unique 03 concentration 

characteristics. These subregions and their unique 

characteristics were found to correspond well with the 

path and frequency of anticyclones. This led to 

the hypothesis that proximity to anticyclones was the 

determining factor in defining the spatial and temporal morphology of the subregions. In the Florida, 

south and mid-Atlantic (and to a lesser degree the 

southwest) subregions, the Ca climatology was hypothesized to be controlled by stagnating conditions 

associated with the westward extension of the quasipermanent Bermuda High. Conversely, the 0 3 climatology of the Great Lakes and northeast subregions is 

thought to be largely controlled by migratory anticyclones (originating in Canada), which because of their 

proximity to the summer jet stream are more transient 

in nature. 

Examination of the unique characteristics exhibited 

by each subregion revealed that concentrations are 

considerably higher in the mid-Atlantic subregion. 

Here, the daily 1-h maximum 03 concentrations 

average 63.23 ppb, with nearly 2% of observations 

exceeding 120 ppb. The south subregion experiences 

the second highest mean daily 1-h maximum concentrations (58.64ppb); however, the distribution of 

concentrations is only weakly skewed (positively), 

therefore values exceeding 120ppb are less likely 

(0.9%) than in several other subregions with lower 

mean concentrations. The southwest (mean daily 1-h 

maximum = 55.15ppb) and northeast (54.61 ppb) 

subregions, while experiencing mean concentrations 

near the domain average (56.06 ppb), show a higher 

propensity for concentrations greater than 120 ppb 

(1.5 and 1.3%, respectively), in response to the 

greater skewness found in the concentration distributions of these subregions. The lowest concentrations 
Spatiotemporal variability of non-urban 0 3 concentrations 2665 

(across all percentiles) are found in the Great Lakes 

(mean daily 1-h maximum= 53.83 ppb) and Florida 

subregions (48.74 ppb). 

Annually, the highest 03 concentrations generally 

occurred during 1988; however, several subregions 

(Florida and the southwest) recorded equally high 

concentrations in other years. No one other year stood 

out statistically across all subregions. Trend analyses 

indicated a slight, though not statistically significant, 

decrease in mean daily 1-h maximum 0 3 concentrations for the northeast, mid-Atlantic and southwest 

subregions and a slight increase over the Great Lakes, 

south and Florida subregions. 

Examination of the principal component scores, as 

well as the actual O a concentrations, revealed a strong 

seasonal cycle in both the Great Lakes and midAtlantic subregions. This seasonality is symmetrical 

around the last week of June and the first week of July, 

respectively. The strength and symmetry of this seasonal cycle is weakened and its timing delayed for both 

the northeast and south subregions, where maximum 

concentrations are delayed until the last week of July 

and the first week of August, respectively. The southwest subregion exhibits a greatly diminished seasonality, with the maximum delayed even further-- 

until the middle of August. The seasonality found in 

the Florida subregion is unique in both its timing and 

strength, as the highest concentrations consistently 

occur during the months of April and May. 

O~one concentrations are more persistent in the 

south (lag1 autocorrelation=0.61), southwest (0.70) 

and Florida (0.64) subregions as autocorrelations are 

significant to lags 3, 3 and 4, respectively. Conversely, 

the autocorrelations are only significant to lag 2 for 

the Great Lakes (0.56) and mid-Atlantic (0.52) subregions and to lag 1 for the northeast (0.47) subregion. 

Spectral Density Analysis supports these findings as 

spectral estimates for the south, southwest and Florida subregions each display classic "red noise" characteristics, indicative of persistence. Conversely, the 

spectral estimates for the remaining three subregions 

display "white noise" characteristics, indicative of 

random features. 

These results have provided a statistically and physically based rationale for choosing distinctive geographical areas for interpreting 03 air quality 

distributions and trends. Since data from stations 

within subregions exhibit homogeneous variability, 

we have been able to develop regionwide 0 3 indicators which have provided meaningful insight into 

the seasonal and annual concentration variability of 

the six subregions. The analysis has also suggested 

that trends analyses for determining general progress 

in improving 0 3 air quality could be based on aggregate statistics from clusters of monitors rather than 

from individual stations. 

Disclaimer--This paper has been reviewed in accordance 

with the U.S. Environmental Protection Agency's policies 

and approved for publication. Mention of trade names or 

commercial products does not constitute endorsement or 

recommendation for use. 

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°o 

APPENDIX 

Station attributes 

Lat. Long. Eiev. 

Station ID MSA name State (deg) (deg) (m) Land-use Meth

010735002 Birmingham AL 33.704 86.669 201 Rural-residential UV 

051191002 Little Rock AR 34.831 92.259 164 Rural-forest UV 

090131001 Hartford CT 41.976 72.387 265 Rural-forest UV 

100031003 Wilmington DE 39.751 75.496 65 Suburban-residential UV 

120250029 Hialeah FL 25.586 80.327 4 Rural-forest UV 

120310077 Jacksonville FL 30.477 81.588 12 Rural-residential UV 

120990007 Boca Raton FL 26.707 80.216 9 Suburban-residential UV 

121035002 Tampa FL 28.090 82.701 2 Rural-residential UV 

121171002 Orlando FL 28.746 81.310 18 Suburban-residential UV 

132151003 Columbus GA 32.539 84.843 122 Rural-agricultural UV 

132470001 Atlanta GA 33.586 84.067 219 Rural-agricultural UV 

170190004 Urbana IL 40.124 88.230 226 Suburban-residential UV 

170973001 Lake County IL 42.290 87.982 223 Suburban-residential UV 

171170002 Not in an MSA IL 39.401 89.811 201 Rural-agricultural UV 

171192007 St Louis IL 38.793 90.040 138 Rural-agricultural UV 

171431001 Peoria IL 40.746 89.586 223 Suburban-residential UV 

172010009 Rockford IL 42.287 89.077 226 Suburban-residential UV 

180970042 Indianapolis IN 39.646 86.249 206 Rural-agricultural UV 

181630013 Evansville IN 38.114 87.536 162 Rural-residential UV 

191131015 Cedar Rapids IA 42.094 91.547 271 Rural-agricultural UV 

191632011 Davenport IA 41.647 90.431 232 Rural-agricultural Chem

201730001 Wichita KS 37.781 97.337 405 Rural-agricultural Chem

210150003 Cincinnati KY 38.918 84.853 171 Rural-agricultural Chem

210190010 Huntington KY 38.444 82.613 174 Suburban-residential Chem

210610500 Not in an MSA KY 37.218 86.074 219 Rural-forest UV 

211390003 Not in an MSA KY 37.115 88.393 110 Rural-agricultural Chem

211850004 Louisville KY 38.389 85.437 240 Rural-residential Chem

220170001 Shreveport LA 32.676 93.860 65 Rural-agricultural UV 

220331001 Baton Rouge LA 30.587 91.207 27 Rural-agricultural UV 

220470002 Not in an MSA LA 30.200 91.100 9 Rural-residential UV 

230052003 Portland ME 43.556 70.213 24 Rural-residential Chem

230112001 Not in an MSA ME 44.225 69.791 55 Suburban-residential UV 

240170010 Washington MD 38.504 76.812 49 Rural-agricultural UV 

250051001 Brockton MA 42.067 71.091 46 Rural-residential UV 

250094003 Lawrence MA 42.814 70.819 6 Suburban-residential UV 

250130008 Springfield MA 42.194 72.556 79 Suburban-residential UV 

260370001 Lansing MI 42.798 84.394 259 Rural-agricultural UV 

260430901 Not in an MSA MI 45.799 87.969 320 Rural-agxicultural UV 

260812001 Grand Rapids MI 43.042 85A13 261 Rural-agricultural UV 

261611001 Ann Arbor MI 42.156 83.778 226 Rural-agricultural UV 
APPENDIX (continued) 

Lat. Long. Elev. 

Station ID MSA name State (deg) (deg) (m) Land-use Meth

290470005 Kansas City MO 39.303 94.376 314 Rural-agricultural Chem

290770026 Springfield MO 37.123 93.263 387 Suburban-residential UV 

310550032 Omaha NE 41.360 96.025 400 Rural-agricultural UV 

340010005 Atlantic City NJ 39.525 74.460 3 Rural-residential Chem

340150002 Philadelphia NJ 39.799 75.218 18 Rural-agricultural Chem

340273001 Newark NJ 40.788 74.677 274 Rural-agricultural Chem

360631006 Niagara Falls NY 43.219 78.479 141 Rural-agricultural UV 

360650004 Utica-Rome NY 43.305 75.722 151 Rural-forest UV 

361173001 Rochester NY 43.230 77.173 140 Rural-agricultural UV 

370510801 Fayetteville NC 35.090 78.792 40 Rural-agricultural UV 

370810011 Greensboro NC 36.113 79.704 229 Rural-residential UV 

371191009 Charlotte NC 35.348 80.692 255 Rural-agricultural UV 

390230001 Dayton OH 40.001 83.804 288 Rural-agricultural UV 

391032001 Cleveland OH 41.155 81.903 323 Rural-residential UV 

391351001 Not in an MSA OH 39.836 84.721 357 Rural-agricultural UV 

400870073 Oklahoma City OK 35.191 97.562 336 Rural-agricultural UV 

401430174 Tulsa OK 35.939 96.003 221 Rural-agricultural Chem

420110/)09 Reading PA 40.321 75.928 81 Suburban-residential UV 

420690100 Scranton PA 41.571 75.511 320 Suburban-residential UV 

421250200 Pittsburgh PA 40.170 80.262 334 Suburban-residential UV 

421330/308 York PA 39.966 76.699 125 Suburban-residential UV 

450450007 Greenville SC 34.901 82.238 287 Rural-agricultural UV 

470370026 Nashville TN 36.155 86.626 186 Rural-forest UV 

470650028 Chattanooga TN 35.104 85.140 62 Rural-forest UV 

470930021 Knoxville TN 36.086 83.762 299 Rural-agricultural UV 

471632002 Johnson City TN 36.541 82.426 487 Rural-residential UV 

480290036 San Antonio TX 29.503 98.540 221 Suburban-residential UV 

481210054 Dallas TX 33.072 96.861 186 Rural-agricultural UV 

482010046 Houston TX 29.827 95.284 16 Suburban-residential UV 

484530014 Austin TX 30.356 97.758 174 Suburban-residential UV 

510610002 Not in an MSA VA 38.474 77.768 102 Rural-agricultural UV 

510850001 Petersburg VA 37.636 77.317 59 Rural-agricultural UV 

511310001 Not in an MSA VA 37.290 75.972 12 Rural-agricultural UV 

511611104 Roanoke VA 37.283 792884 346 Suburban-residential UV 

540690007 Wheeling WV 40.121 80.700 212 Suburban-residential UV 

550890005 Milwaukee WI 43.321 87.941 195 Suburban-residential UV 

551171002 Sheboygan WI 43.669 87.740 202 Rural-agricultural UV 

* l--Great Lakes subregion, 2--northeast subregion, 3--south subregion, 4--mid-Atlantic subregion, 5--southwest subregion, 6--Flo