AtmosphericEnvironmentVol. 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 ModelingDivision,AirResources Laboratory, National Oceanic and Atmospheric

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

JERRY M. DAVIS Department ofMarine, Earth and Atmospheric Sciences,North Carolina State University,Raleigh, NC 27695, U.S.A.


### and

PETER BLOOMFIELD Department ofStatistics, North Carolina State University,Raleigh, NC 27695, U.S.A.

(First received6 October 1992and in final form 12April 1993)

Abstract--The spatial and temporal variabilityof the daily 1-h maximum 0 3 concentrationsover nonurbanareasoftheeastern UnitedStates ofAmericawas examinedfor theperiod 1985-1990 usingprincipal component analysis. Utilization of Kaiser's Varimax orthogonal rotation led to the delineation of six contiguous subregionsor "influence regimes" which together accounted for 64.02% ofthe total variance. Each subregiondisplayed statistically unique03 characteristics and correspondedwell with the path and frequency of anticyclones. When compared to the entire domain, the mid-Atlanticand south subregions observehighermeandaily 1-hmaximumconcentrations.Concentrationsare nearthe domainaverage for the northeast and southwest subregions and are lowest in the Great Lakes and Florida subregions. The percentageofobservationsexceeding 120ppb were greatest inthe mid-Atlanticand southwest subregions, nearthe domainaverage inthenortheastand south subregions,andlowest intheGreat LakesandFlorida subregions. Examinationofthetimeseriesoftheprincipalcomponentscores associated withthesubregionsindicated that Great Lakes and mid-Atlanticsubregions tend to observe a stronger seasonal cycle, with maximum concentrationsoccurringduringthelastweekinJuneandfirstweekinJuly,respectively.Thestrengthofthis seasonalityisweakened for the northeastand south subregionsanditstimingdelayed, untiltheendofJuly and the first ofAugust,respectively. The southwest subregionexperiences a greatly diminishedseasonality, with maximumconcentrationsdelayed untilthe middle ofAugust. The seasonalityfound in the Florida subregionis uniqueinboth its strength andtiming,as thehighest concentrationsconsistentlyoccurduring the months ofApriland May. The time series were thendeseasonalized and autocorrelationsand spectral densityestimatescalculated, revealingthat persistence ismuchmoreprevalentintheFlorida(autocorrelation significant to a lag of 4 days), south (3 days) and southwest (3 days) subregions. Conversely, autocorrelationsare onlysignificant to a lagofoneday inthe northeastandtwo daysfor the Great Lakes and mid-Atlanticsubregions. Key word index: Ozone, principalcomponent analysis, influence regimes, time series analysis, persistence, seasonality,anticyclones.

l. INTRODUCTION Traditionally,ozone (Oa) has beencharacterized 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 injuryare becoming growing environmental concerns (Lefohn

• On assignmentto theAtmosphericResearch andExposure Assessment Laboratory, U.S. EnvironmentalProtection Agency. and Lucier, 1991). Daily maximum 0 3concentrations 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 millionsofkilograms oftobacco in southern Ontario. More recently, Reich and Amundson (1985) have shown that exposure to 03 concentrations as low as 50 ppb is sufficientto cause damage to certain species of vegetation. Additionally, the Office of Technology
26452646 B.K. EDER et al.

Assessment (OTA, 1989), in a review ofthe effects of 0 3 on crops in the eastern United States ofAmerica, determined that 03 reduces yields of economically important crops from between 1 and 20%, and that annual benefits ofbetween $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 ofAmerica, 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 80ppb 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 ofconcentrationswithin urban areasis also well known(Angleand 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 120ppb only when there was a significant regional contribution (on the order of 80-85 ppb). In a similar study ofDetroit, 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 ofthe 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 ofAmerica; 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 ofthe 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 of0 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 offorcing 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 SO2- concentrations inprecipitation (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 physicallybased. 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 homogeneousO3characteristics, this approach allows us to develop regionwide indicators that should provide meaningfulinsight into the variability of03 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 ofanomalous
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 ofthe eastern United States of America. Such analysis will hopefully act as a catalystfora varietyofinterpretive studies designed to explicate these characteristics and their relationships with emissions and meteorology patterns.

2. 0 3DATA
Thedata employedinthis analysiswere 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 perbillion--ppb), collected bylocal agencies at nearly 800stations nationwide. The data aremonitored 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 ofthis study was the establishment and utilization of a complete, regionally representative 0 3 database (unencumbered by missing data or local-scale variability) that would proveSpatiotemporal variability ofnon-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, designedtoprotect 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 basedon thismeasurement. Additionally,Lefohn et al. (1990) found that when assessingthe adverseeffectsof 03 on human health and vegetation, concentrations found in the upper end ofthe distributioncurve are of greatest concern. Utilization of the daily 1-h maximum concentration also helps minimize local-scale variabilitybecause as Decker et al. (1976) have shown, at the time of maximum surface concentration (typicallybetween 1and 3 p.m. LST),theboundarylayeris generallyuniformly mixed and the surface concentration is therefore representative ofthe mean boundary layer concentration (Vukovich and Fishman, 1986). Second, to minimize chemical scavenging by NO found in urban areas, only those stations classifiedas either rural or suburban and reporting a land use of either forest, agriculture or residentialwere 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 spatiallyrepresentative 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 betterfor 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 acrossthe easternhalfofthe United States, the majority of which (55), are classified as rural (Fig. 1). Additionalinformation concerning attributes

• Rural Site
• Suburban Site

Fig. 1. Aerometric information retrieval system (AIRS)

sites used in the analysis. ofthe stations,which are alllocated at low elevations, is provided in the Appendix. Several combinations of station "seasons" (season isdefinedasthe periodfrom 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 ofobservations (98,868) and a total capture rate of 95.7%, which translates into an averageof205 of214 daysper station "season".Allmissing data were replaced using a linear interpolation scheme, across time. Although RPCA can be applied to non-normally distributeddata (Rummel, 1970),the resultingcorrelation matrix (covariance matrix) used in the analysis may bepoorlydefinedwhen, forexample,the data are extremely skewed such that a very few extreme values dominate the resultingcorrelation coefficients.Examination ofthe probabilitydistribution 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 subjectedto a loglo transformation, as suggested by Holland and Fitz-Simmons(1982) were usedinthe 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

COMPONENTANALYSIS
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 descriptiveconclusions can be made. This is achieved through the sorting of initiallycorrelated data into a hierarchy of statistically independent modes of variations which explain successivelyless 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 ofa square, symmetricalcorrelationmatrix R (having dimensions of77 × 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 ofa 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 correlationcoefficientbetween the component and the2648 B.K. EDERet aL

individual station. By using R and the identity matrix (I), ofthe 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 respective

### eigenvalues represent the amount of variance explained by each ofthe eigenv¢ctors. By retaining only the first feweigenvector-eigenvaluepairs, orprincipal components, a substantial amount of the total variance can be explained while ignoring the higher order principal components which explain minimal amounts ofthe variance and can therefore be viewed
as noise. When the elements of each eigenvector are multiplied by the square root ofthe associated eigenvalue,
one obtains the 

### principal component loading, which represents thecorrelation between the component and the station. (The square of a component loading is called the

### communality and indicates theproportion 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 ofthe 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 3concentration and the component loading.
3.2. 

### Number ofcomponents 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 whichrepresent nothingmore than randomly generated variations (Richman, 1981). Application of the Scree test (Cattell, 1966), which entails the plotting of the percentage variance explained byeach component in the order extracted and then lookingfora discontinuity inthecurve, 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 3concentration 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 ofvariance explained) with this seventh component; subsequently, the six component solution was selected. The fraction oftotal 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 therebyindependent variables (principal components) and yet still explains almost two-thirds of the total variance. These results are verypromising, 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 tendencyfor 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 ofone sign), which supports thehypothesisthat 0 3has a strong regionalscalecomponent. 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 occurduring theperiod fromMayto August. Scores associated with the months of April and especially September and October are consistentlynegative (associated with low 0 3concentrations for stations with positive loadings). This nearly regionwide, seasonal-scale cycle is no doubt responding

Table 1. Statisticsfor the first six unrotated principal components

PC 1 PC2 PC 3 PC4 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 64.02Spatiotemporal variability ofnon-urban Oa concentrations 2649

6O
7O

65

5 49 40 

### PRINCIPALCOMPONENT ONE (35.86%)



### (a)


# I
li 

# oi -1i -2 -3i _4i _5i (b)

i

...... i

...... 1~5 ...... i

...... 1987 1968 1900
Fig. 2. (a)Component Ioadingsand (b)daily timeseries(for 1April-31 October) ofthe standardized component scoresassociated withthe first

non-rotated principal component.

primarily to the constant annual cycle ofthe 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 ofthe study area. Here, 03 concentrations tend to be highest when the principal component
scores are negative (i.e. April, September and October). Other features ofthis first componentinclude the dominanceof 1988inthe numberofPC scoresabove3 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 ofthese higher order non-rotated principal components is beyond the current scope ofthis 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 ofthe non-rotated components. Because of this, non-rotated PCA is used mainly for data reduction and can be somewhat misleadingwhen used for spatial delineation.

3.4. Rotated solution Since one of the major goals of this paper was to delineate areas ofhomogeneous 03 concentrations, a rotation wasperformed onthecomponentsin orderto better segregatethe 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 theconstraints that theindividual componentsremain orthogonal or uncorrelated to each other. This method increases the segregationbetween component2650 B.K. 

### EDER et aL loadings, whichin turn better defines a distinct grouping or clustering ofintercorrelated 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



### ROTATEDPRINCIPAL ~ ROTATED PRINCIPAL COMPONENT

ONE ~ ~ COMPONENT TWO 80 f'~

"60

5 6

." b ~ Component Loadlngs(xlO0)



### ROTATEDPRINCIPAL /~ ROTATED PRINCIPAL (~ COMPONENT

FOUR ~ ~ op~

~_~ Component

Load~gs (xlO0)

R Oo~ DP R~NFC/PEAL

_ f~ R OC, o~AT~ DP:/NsI C';AL

_

Fig. 3. Component Ioadings associated withthe six rotated principal components.Spatiotemporalvariabilityofnon-urban 03 concentrations 2651

Table 2. Statistics for the first sixrotated principalcomponents

PC 1 PC 2 PC 3 PC4 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

areaencompassing GreatLakes regionnorth and west ofthe Ohio River. The second component encompasses the northeast States from Pennsylvania and northern New Jersey, northward. The highest loadings associatedwith 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 ofthe study area from Texas and Louisiana northward to Kansas, and finallyloadings associated with the sixthcomponent highlightstationsinFlorida. 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 ofthe most apparent features ofFigs 3 and 4 is the regional nature and strength of all six rotated components. Each component identifiesan "influence regime" that distinguishes a different and well segregated area ofthe study domain. The subregions, as delineatedby the 0.40 component loadingisopleth 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 illustratesthe sharpness ofthe station segregationand 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 ofsome cross-boundarycorrelation. 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 attributableto this site'selevation(at 487 m this isthe highest of all sites), and the unique 03 characteristics often observed at higher elevationsites(Anejaand 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-subregioncorrelation existed,the orthogonality constraintwas relaxedwhen rotating the components. 6

LANT/C'

MaximumComponentLoadings(xlO0)
Fig. 4. Sixhomogeneous 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 ofthe variance ofthe component loadingsbetween 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 ifthe correlationexhibitedbetween 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 ofthe orthogonal rotation and suggeststhat the physicalfactorsdetermining the uniqueness of03 concentrations for each ofthese subregions are indeed different.

4. TIME SERIES ANALYSIS Having delineated six homogeneous O3 concentration patterns, elucidation ofthe temporal variance of each of these subregions can now be performed through an examination ofthe rotated principalcomponent 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-N2652 B.K. EVER et al.

a mean ofzeroand standard deviation ofone. 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 0

--1'

i

...... J

.....



## 11 0! -1 ! -2i

-4 -5

...... i

......
e O:EATtail8

...... 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 areused 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).Spatiotemporalvariabilityofnon-urban03 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. Dailytimeseriesofthe standardizedprincipalcomponentscoresassociatedwith the sixhomogeneoussubregions.

simulate eachday; subsequently, modeling is confined to selected episodes during which the highest 03 concentrations are observed. Our results will allow modelers, upon quick examination ofthe 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 seriesrevealsthat in2654 B.K. EDERet 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, signifyingthe most intense episode experienced bythe 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 LAKESSUBFtE~ON 

# I ,h I, I,,

MAY dUN ~ AUG SEP OCt

MONTH 

### NORTHEASTSI,,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 variabilityofnon-urban03 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 principalcomponent scores associated with the six homogeneous subregions as defined by the median scores for the six year period. A cubic splinefunctionwas 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 calculatingthe median
score for each day across the six year period. The median was selected as opposed to the mean to prevent the dominanceofany one year indetermining the typical season. These median scores were then smoothed utilizing a cubic spline smoother (Reinsch, 1967), revealingthat each subregiondisplays a unique2656 B.K. EDER ef at.



### 03 climatology, with variations in the strength, symmetry and timing ofthe seasonal-scale cycles (Fig. 6). To facilitatecomparison 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 oftotal 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 bydefinition have a mean of zero and a variance of one (Table 3). TheGreat 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 cyclewith positive
scoresbeing symmetrical around the last week inJune and the first week ofJuly, respectively. The strength and symmetry ofthis seasonality is weakened and its timing delayed forboth the northeast (ssv= 12%) and south (ssv= 7%) subregions. Maximum scores for the northeast subregion tend to occurduring thelast week ofJuly, 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 LUnO 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 componentscoresforeach of

the sixhomogeneous subregions.

fromeach oftheir sixyears ofraw scores. Autocorrelations (toalag of20 days)were then calculated foreach 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 foreach lag and subregion and areplotted 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 of3, 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 ofthe 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 of2 claysfor the Great Lakes and mid-Atlantic suhregions and to a lag of 1day for the northeast subregion. LagI correlations were also lower,ranging from0.56inthe Great Lakes subregion,

Table 3. Segregation oftotal 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 

### 0.67Spatiotemporal variability of non-urban Oa concentrations 2657

0.8 0.7 0.6 _~0.5 

# ~0.4 0.3

~ 0.2 00.1 0.0 -0.1 -0.2 GREATLAKESSUBREGION 

## 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 NORTHEASTSUBREGION

2 3 4 5 6 7 8 9 1011 121314151617181920

LAG

1 C SOUTHSUBREGION

2 3 4 5 6 7 8 91011121314151617181920 LAG z 0

uu 

## 8 MID-ATLANTICSUBREGION

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 SOUTHWESTSUBREGION

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 FLORIDASUBREGION

2 3 4 5 6 7 8 91011121314151617181920

LAG 

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

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, suchthat extended periods ofhigh 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 ofsine and cosine
waves of varying amplitudes and wavelengths, yielding a measure ofthe distribution ofthevariance 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 areasthat 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 ofsimple 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 Oa 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 

# °I

0.7 

# ~0.6
0.5



### 0



# i

o 6 12 18 24

6 12 18

6 12 18 ~qEAT LAKESSt~: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

1~10e0.7 

# ~ 0.6 0.5 Spatiotemporalvariabilityofnon-urban 0 3concentrations

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~



# J0.8- 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 subregionswould tend to experience more exceedinglyhigh 03 concentrations (relative to their respective mean concentrations), while the south would tend to experience less high concentrations. The kurtosis values ranged from 0.312660 B.K. EDER et al.

Table 4. Summary statistics ofthe daily 1-h maximum 03 (ppb)concentrations associated witheach 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. BoxplotsoftheOaconcentrations associatedwith the sixhomogeneous 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 120ppb (1.9%) are also the greatest in this subregion. Translated into a typical season, 03 concentrations in this homogeneous region exceed 120ppb (the NAAQS)4 times, and they exceed 80ppb (a threshold above which 0 3 contributes to forestdecline (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 isconsistent 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 ofdays exceeding 80ppb (15.0%--ranked 2nd) and 120ppb (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 positivelyskewed, subsequently, their 99th percentiles rank second and third, respectively. This is again reflected inthe number ofdays exceeding 80ppb (14.0% forthe southwest--ranked 3rd, and 13.1% for the northeast --ranked 4th) and 120ppb (1.5% forthe southwest-- ranked 2nd and 1.3% forthe northeast--ranked 3rd). This indicates that while concentrations are typically
near the domain averageinthese 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)inthe form ofboxplots, Analysisofthe 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%: 125.1ppb) 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-

### Spatiotemporalvariabilityofnon-urban O3concentrations 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-EASTSUBREGaON

O

O O

o o o

dUN dUL AUG SEP

SOUTHSUl3REG~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 130.0ppb), July (65.8, 80.0, 94.9 and 129.2ppb) and August (65.1, 79.9, 92.9 and 127.4pph). 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 60ppb (with the exception of September and October when they are lower). The 90th% does show a propensity for being slightly higher during June-August (90.0ppb). 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,wecan nowfocusontheannual variability
as seen in Fig. 12. Within each subregion, the years were ranked in descending order based upon the higherpercentile statistics (fromthe mean andmedian to the 99th%). For the Great Lakes subregion, 1988
was theworst yearforhigh Oa concentrations, followed by 1987. The remaining years were more closely2662 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 ofthe annual Os concentrations associated with the six homogeneous subregions. o

o



### groupedand are,indescending 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, 1989and 1990. For the south 

### subregion, 1988
was again the worst year. This was followed 

### by 1990 and 1987, in which the 

### 99th% and 95th% werehigher in 

### 1990,butthe75th, mean and median were higher in 1987. The remaining years in descending order were 

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

### subregion, the worstyear, 1988, was followed 

### by 1987,then 1985, 1986, 1990and finally 1989. Ranking the years was 

### a little more difficult for the southwest 

### subregion. AIthough 1988 recorded the overall highest concentrations,itrecorded thelowest 99th % ofall sixyears.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 ofnon-urban O3 concentrations

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

domain. (Standard error ofestimate 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)
*Significantat 95%. f Significantat 90%.

ard errors) ofthe 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 ofslopes 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 increasein the south subregion (across all statistics) and the Great Lakes and Florida (mean and median), only2 ofthe 35statistics exhibited significant trends (90% C.L.), which given the number oftests, 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 ofpossible causal relationships withboth meteorological and emissions patterns. However, in a climatological study ofnon-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 ofthe United States of America east of90°W and south of40°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 ofthe United States ofAmerica 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 andmid-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 ofthe 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 ofmaximum

### 2664 B.K. EnERet al.



### stagnation shifts southwestward, resulting in an elongated maximumstretching fromMississippi back to North Carolina. This deviation is also well represented in the time series associated with the south subregion,wheremaximum03 concentrationstendto 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 subregionsgenerallysupportthe hypothesisthat stagnating conditions associated with the westward extension ofthe 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 correspondswellwiththis study'snortheast 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 summerjet 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 generallyoriginateinwesternCanada and traversethe northern portion of the United States of America, generally along the 40th parallel. On the rare occasions when these migratory anticyclones do move south of40°N, they generally undergo airmass modification and become amalgamated with the western extension ofthe Bermuda High. (This transformation oftenoccursinthemid-Atlanticsubregion and maybe 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 maybeattributabletofindings byKingand 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 increasedresidence times(thetimethe airparcelsremain under the influence ofthe anticyclone and its accompanying favorable meteorological conditions), is reflected in Fig. 10,where comparedto the Great Lakes subregion, ozoneconcentrations 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 ofa 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 Varimaxrotation led to the identification ofsix separate,contiguous subregions or "influence regimes"that each exhibit statistically unique 03 concentration characteristics. These subregions and their unique characteristicswerefound to correspond wellwiththe path and frequency of anticyclones. This led to the hypothesis that proximityto 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 ofthe quasipermanent Bermuda High. Conversely,the 0 3climatologyofthe Great Lakesand northeast subregions is thought to belargelycontrolledbymigratoryanticyclones (originating in Canada), which because oftheir proximitytothe summer

jet stream aremoretransient in nature. Examination ofthe unique characteristicsexhibited by each subregion revealed that concentrations are considerably higher in the mid-Atlantic subregion. Here, the daily 1-h maximum 03 concentrations average 63.23ppb, with nearly 2% of observations exceeding 120ppb. The south subregion experiences the second highest mean daily 1-h maximumconcentrations (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 (54.61ppb) subregions, while experiencing mean concentrations
near the domain average (56.06ppb), show a higher propensity for concentrations greater than 120ppb (1.5 and 1.3%, respectively), in response to the greater skewness found in the concentration distributions of these subregions. The lowest concentrationsSpatiotemporal variabilityofnon-urban0 3concentrations 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 concentrationsinother 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 wellas the actual O aconcentrations,revealed a strong seasonal cycle in both the Great Lakes and midAtlantic subregions. This seasonality is symmetrical around the last week ofJune and thefirst week ofJuly, respectively. The strength and symmetry of this seasonalcycleisweakened and itstimingdelayedfor both the northeast and south subregions, where maximum concentrations are delayed until the last week ofJuly and the first week ofAugust, 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 subregionis 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 significantto 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 1for the northeast (0.47) subregion. Spectral Density Analysis supports these findings as spectral estimates for the south, southwest and Florida subregionseach 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 statisticallyand 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 inimproving0 3air 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 recommendationfor use.

REFERENCES
Altshuller A. P. (1986) Relationships between direction of wind flow and ozone inflow concentrations at rural locations outside of St Louis. MO. Atmospheric Environmeat 20, 2175-2184. Aneja V. P. and Li Z. (1992)Characterization of ozone at high elevation in the Eastern United States: trends, seasonal variations, and exposure. J. geophys. Res. 97, 9873-9888. AngleR. P. and SandhuH. S.(1986)Ruralozoneconcentrations in Alberta, Canada. Atmospheric Environment 20, 1221-1228. Ashbaugh L. L., Myrup L. O. and FlocchiniR. G. (1984)A principalcomponent analysisof sulfur concentrationsin the western United States. Atmospheric Environment 18, 783-791. Box G. E. P. and JenkinsG. M. (1970) Time SeriesAnalysis: Forecastingand Control. Holden-DayInc., SanFrancisco, CA. Buell C. E. (1975)The topography ofempirical orthogonal functions. Preprints of the Fourth Conference on ProbabilityandStatistics inAtmospheric Sciences, pp. 188-193. AmericanMeteorological Society, Boston. Cattell R. B. (1966)The scree test for the numberoffactors. Multivar. Behav. Res. 1, 245-276. Chameides W. L., Lindsay R. W., Richardson J. and Kiang C. S. (1988)The role ofbiogenichydrocarbons in urban photochemicalsmog:Atlantaas a case study. Science241, 1473-1475. ChevoneB.I. and LinzonS.N. (1988)Tree decline inNorth America. Envir. Pollut. 50, 87-101. CoxW.M. andClarkJ. (1981)Ambientozoneconcentration patterns among eastern United States urban areas using factor analysis. J. Air Pollut. Control Ass. 31, 762-766. CrutcherH. L.,Rhodes R.C.,GravesM. E.,FairbairnB.and Nelson A. C. (1986) Application of cluster analysis to aerometricdata. J. AirPollut. ControlAss. 36, 1116-1122. DeckerC.E.,RippertonL.A.,WorthJ.J.B.,VukovichF.M., BachW.D.,TommerdahlJ. B.,SmithF.andWagnerD.E. (1976)FormationandtransportofoxidantsalongtheGulf Coast andinthenorthernU.S.EPA Report No. 450/3-76- 033, U.S. EnvironmentalProtection Agency,OfficeofAir Quality Planning and Standards, Research Triangle Park, NC. Eder B. K. (1989)A principalcomponent analysisof SO2- precipitation concentrations over the Eastern United States. Atmospheric Environment 23, 2739-2750. EderB.K.,DavisJ. M. andMonahanJ. F.(1987)Spatialand temporal analysis ofthe Palmer Drought Severity Index over the Southeastern United States. J. Clim. 7, 31 56. Harris C. W.and KaiserH. F. (1964)Obliquefactor analytic solutions by orthogonal transformations. Psychometrika 29, 347-362. HollandD. M. andFitz-SimmonsT.(1982)Fittingstatistical distributionsto air quality data by the maximum likelihood method. Atmospheric Environment 16, 1071-1076. HorelJ. D. (1981)Arotated principalcomponentanalysisof the interannual variability of the Northern Hemisphere 500mb height field. Mon. Weath. Rev. 109,2080-2092. Kaiser H. F. (1958) The Varimax criterion for analytical rotation in factor analysis. Psychometrika 23, 187-201. Kelly N. A., Ferman M. A. and Wolff G. T. (1986) The chemical and meteorological conditions associated with high and low ozone concentrationsin Southeastern Michigan and nearbyareas ofOntario.J. Air Pollut. Control Ass. 36, 150-158. King W. J. and Vukovich, F. M. (1982) Some dynamic2666 B.K. EDEnet aL

aspects ofextendedpollutionepisodes. AtmosphericEnvironment 16, 1171-1181. Korshover J. (1975) Climatology of stagnating anticyclones east of the Rocky Mountains, 1936-1975. NOAA Technical Memo. ERL ARL-55, 26 pp. Korshover J. and Angell J. K. (1987) Annual summary: air stagnation casesin the Eastern United States during 1985. Mon. Weath. Rev. 115, 336-341. KorsogP.E.and WolffG.T.(1991)Anexaminationofurban
ozonetrends in theNortheastern U.S. (1973-1983) usinga robust statistical method. Atmospheric Environment 25B, 47-57. Lefohn A. S. and Lucier A. A. (1991) Spatial and temporal variability of ozone exposure in forested areas of the United States and Canada: 1978-1988. J.Air WasteMan. Ass. 41, 694-701. LefohnA.S.and ShadwickD. S.(1991)Ozone,sulfurdioxide, and nitrogen dioxide trends at rural sites located in the United States. Atmospheric Environment 25A, 491-501. LefohnA.S.,Knudsen, H.P. and McEvoyL.R.Jr(1988)The
use ofkriging to estimate monthly ozone exposure parameters for the Southeastern United States. Envir. Pollut. 53, 189-224. Lefohn A. S.,BenkovitzC. M.,Tanner R. L., Shadwick D. S. and Smith L. A. (1990) Air quality measurements and characterization for terrestrial effects research. In Acidic Deposition: State of Science and Technology , Report 7, National Acid Precipitation Assessment Program, Washington, D.C. Lindsay R. W. and Chameides W. L. (1988) High ozone events in Atlanta, Georgia, in 1983and 1984. Envir. Sci. Technol. 22, 426-431. Linzon S.N.(1986)Effects ofgaseouspollutants on forests in eastern North America. WaterAirSoilPoUut. 31, 537-550. Liu S. C., Trainer M., Fehsenfeid F. C., Parrish D. D., Williams E.J., Fahey D. W., Hubler G. and Murphy P. C. (1987) Ozone production in the rural troposphere and the implications for regional and global ozone distribution. J. oeophys. Res. 92, 4191-4207. LoganJ. A.(1989)Ozone in rural areas ofthe United States. J. oeophys. Res. 94, 8611-8532. MacDowellF. D. H.,Mukammal E.I. and ColeF. W. (1964) Direct correlation of air pollution ozone and tobacco weather fleck. Can. J. Plant Sci. 44, 410-417. McLaughlin S. B. (1985)Effects ofair pollution on forests: a critical review. J. Air Pollut. ControlAss. 35, 512-534. Meagher J. F., Lee N. T., Valente R. J. and Parkhurst W. J. (1987) Rural ozone in the southeastern United States. Atmospheric Environment 21, 605-615. Mukammal E.I.(1960)Meteorologicalconditionsassociated with flecking oftobacco leaves during 1959.Meteorological Branch, Canada Dept. of Transport., Technical Circ. 3362-239. OTA (1989) Catching Our Breath: The Next Steps For Reducing Urban Ozone. Office ofTechnologyAssessment. OverlandJ. E. and Preisendorfer R. W. (1982)A significance test for principal components applied to a cycloneclimatology. Mon. Weatb. Rev. 110, 1-4. Reich P. B. and Amundson A. G. (1985) Ambient levels of ozone reduce net photosynthesis in tree and crop species. Science 230, 566-570. Reinsch C. H. (1967) Smoothing by spline functions. Numerische Mathematik 10, 177-183. Richman M. B. (1981) Obliquely rotated principal components: an improved meteorological mapping technique? J. appl. Met. 20, 1145-1159. Richman M. B. and Lamb P. J. (1985) Climate pattern analysis of three- and seven-day summer rainfall in the central United States: some methodological considerations and a regionalization. J. Clim. appl. Met. 24, 1325-1343. Rummel F. J. (1970)Applied FactorAnalysis. Northwestern University Press, Evanston, Illinois. Tang R. T., Gerry S. C., Newsom J. S., Van Meter A. R., Wayland R. A., Godowitch J. M. and Schere K. L. (1990) User's Guide for the Urban Airshed Mode. Volume V: Description and operation of the ROM-UAM interface program system. EPA-450/4-90-007E, Office ofAir Quality Planning and Standards, Research Triangle Park, NC, 168 pp. United States Environmental Protection Agency (1985) Quality Assurance requirements for state and local air monitoring stations (SLAMS). 40 CFR, Part 58, Appendix A. United States Environmental Protection Agency(1990) National Air Quality and Emissions Trends Report, 1988. Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-450/4-90-002. Vukovich F. M. and Fishman J. (1986) The climatology of summertime Oaand SO2 (1977-1981). Atmospheric Environment 20, 2423-2433. VukovichF. M., BachW. D.,Crissman B.W. and KingW.J. (1977)On therelationshipbetweenhigh ozonein therural surface layer and high pressure systems. Atmospheric Environment 11, 967-983. World MeteorologicalOrganization (1966)ClimateChange. Technical Report No. 79. WMO No. 195, 100 pp. Young J. O., Aissa M., Boehm T. L., Coats C. J., Eichinger J. R., Grimes D. J., Hallyburton S. W., Heiiman W. E., Olerud D. T. Jr, Roselle S. J., Van Meter A. R., Wayland R.A.and PierceT. E. (1989)Developmentofthe Regional Oxidant Model Version 2.1. EPA/600/3-89/044, Atmospheric Research and Exposure Assessment Laboratory, Research Triangle Park, NC, 55 pp. Zishka K. M. and Smith P. J. (1980) The climatology of cyclones and anticyclones over North America and surrounding oceanenvironsforJanuary and July 1950-1977. Mon. Weath. Rev. 108, 387-401.°o APPENDIX

Station attributes

Lat. Long. Eiev. Data capture Station ID MSA name State (deg) (deg) (m) Land-use Method (%) Subregion*/communality
010735002 Birmingham AL 33.704 86.669 201 Rural-residential UV 97.0 3/0.45 051191002 Little Rock AR 34.831 92.259 164 Rural-forest UV 99.0 5/0.30 090131001 Hartford CT 41.976 72.387 265 Rural-forest UV 97.3 2/0.72 100031003 Wilmington DE 39.751 75.496 65 Suburban-residential UV 90.0 4/0.34 120250029 Hialeah FL 25.586 80.327 4 Rural-forest UV 94.0 6/0.44 120310077 Jacksonville FL 30.477 81.588 12 Rural-residential UV 94.4 6/0.42 120990007 Boca Raton FL 26.707 80.216 9 Suburban-residential UV 94.9 6/0.55 121035002 Tampa FL 28.090 82.701 2 Rural-residential UV 98.6 6/0.36 121171002 Orlando FL 28.746 81.310 18 Suburban-residential UV 95.1 6/0.58 132151003 Columbus GA 32.539 84.843 122 Rural-agricultural UV 97.0 3/0.35 132470001 Atlanta GA 33.586 84.067 219 Rural-agricultural UV 95.7 3/0.37 170190004 Urbana IL 40.124 88.230 226 Suburban-residential UV 98.1 1/0.58 170973001 Lake County IL 42.290 87.982 223 Suburban-residential UV 96.7 1/0.67 171170002 Not in an MSA IL 39.401 89.811 201 Rural-agricultural UV 95.6 1/0.46 171192007 St Louis IL 38.793 90.040 138 Rural-agricultural UV 98.0 1/0.37 171431001 Peoria IL 40.746 89.586 223 Suburban-residential UV 98.7 1/0.67 172010009 Rockford IL 42.287 89.077 226 Suburban-residential UV 94.4 1/0.72 180970042 Indianapolis IN 39.646 86.249 206 Rural-agricultural UV 94.4 1/0.44 181630013 Evansville IN 38.114 87.536 162 Rural-residential UV 94.3 1/0.30 191131015 Cedar Rapids IA 42.094 91.547 271 Rural-agricultural UV 96.3 1/0.58 191632011 Davenport IA 41.647 90.431 232 Rural-agricultural Chemil. 97.5 1/0.62 201730001 Wichita KS 37.781 97.337 405 Rural-agricultural Chemil. 95.6 5/0.27 210150003 Cincinnati KY 38.918 84.853 171 Rural-agricultural Chemil. 95.6 3/0.29 210190010 Huntington KY 38.444 82.613 174 Suburban-residential Chemil. 94.8 3/0.30 210610500 Not in an MSA KY 37.218 86.074 219 Rural-forest UV 94.7 3/0.42 211390003 Not in an MSA KY 37.115 88.393 110 Rural-agricultural Chemil. 93.9 3/0.32 211850004 Louisville KY 38.389 85.437 240 Rural-residential Chemil. 96.0 3/0.29 220170001 Shreveport LA 32.676 93.860 65 Rural-agricultural UV 96.1 5/0.41 220331001 Baton Rouge LA 30.587 91.207 27 Rural-agricultural UV 98.4 3/0.23 220470002 Not in an MSA LA 30.200 91.100 9 Rural-residential UV 97.2 5/0.25 230052003 Portland ME 43.556 70.213 24 Rural-residential Chemil. 94.1 2/0.67 230112001 Not in an MSA ME 44.225 69.791 55 Suburban-residential UV 90.5 2/0.64 240170010 Washington MD 38.504 76.812 49 Rural-agricultural UV 97.2 4/0.56 250051001 Brockton MA 42.067 71.091 46 Rural-residential UV 93.5 2/0.66 250094003 Lawrence MA 42.814 70.819 6 Suburban-residential UV 95.2 2/0.59 250130008 Springfield MA 42.194 72.556 79 Suburban-residential UV 95.6 2/0.69 260370001 Lansing MI 42.798 84.394 259 Rural-agricultural UV 97.7 1/0.56 260430901 Not in an MSA MI 45.799 87.969 320 Rural-agxicultural UV 97.6 1/0.45 260812001 Grand Rapids MI 43.042 85A13 261 Rural-agricultural UV 95.8 1/0.58 261611001 Ann Arbor MI 42.156 83.778 226 Rural-agricultural UV 92.5 1/0.49 O

"O O

~a O"

O



### 8

g~ OAPPENDIX (continued)

0o

Lat. Long. Elev. Data capture Station ID MSA name State (deg) (deg) (m) Land-use Method (%)
290470005 Kansas City MO 39.303 94.376 314 Rural-agricultural Chemil. 94.7 290770026 Springfield MO 37.123 93.263 387 Suburban-residential UV 94.0 310550032 Omaha NE 41.360 96.025 400 Rural-agricultural UV 94.9 340010005 Atlantic City NJ 39.525 74.460 3 Rural-residential Chemil. 96.5 340150002 Philadelphia NJ 39.799 75.218 18 Rural-agricultural Chemil. 97.6 340273001 Newark NJ 40.788 74.677 274 Rural-agricultural Chemil. 95.0 360631006 Niagara Falls NY 43.219 78.479 141 Rural-agricultural UV 98.0 360650004 Utica-Rome NY 43.305 75.722 151 Rural-forest UV 97.7 361173001 Rochester NY 43.230 77.173 140 Rural-agricultural UV 96.4 370510801 Fayetteville NC 35.090 78.792 40 Rural-agricultural UV 94.0 370810011 Greensboro NC 36.113 79.704 229 Rural-residential UV 94.0 371191009 Charlotte NC 35.348 80.692 255 Rural-agricultural UV 96.6 390230001 Dayton OH 40.001 83.804 288 Rural-agricultural UV 96.9 391032001 Cleveland OH 41.155 81.903 323 Rural-residential UV 96.5 391351001 Not in an MSA OH 39.836 84.721 357 Rural-agricultural UV 96.7 400870073 Oklahoma City OK 35.191 97.562 336 Rural-agricultural UV 94.4 401430174 Tulsa OK 35.939 96.003 221 Rural-agricultural Chemil. 96.2 420110/)09 Reading PA 40.321 75.928 81 Suburban-residential UV 96.0 420690100 Scranton PA 41.571 75.511 320 Suburban-residential UV 97.9 421250200 Pittsburgh PA 40.170 80.262 334 Suburban-residential UV 95.4 421330/308 York PA 39.966 76.699 125 Suburban-residential UV 94.3 450450007 Greenville SC 34.901 82.238 287 Rural-agricultural UV 92.3 470370026 Nashville TN 36.155 86.626 186 Rural-forest UV 94.3 470650028 Chattanooga TN 35.104 85.140 62 Rural-forest UV 99.1 470930021 Knoxville TN 36.086 83.762 299 Rural-agricultural UV 97.0 471632002 Johnson City TN 36.541 82.426 487 Rural-residential UV 90.9 480290036 San Antonio TX 29.503 98.540 221 Suburban-residential UV 96.3 481210054 Dallas TX 33.072 96.861 186 Rural-agricultural UV 93.0 482010046 Houston TX 29.827 95.284 16 Suburban-residential UV 96.1 484530014 Austin TX 30.356 97.758 174 Suburban-residential UV 91.9 510610002 Not in an MSA VA 38.474 77.768 102 Rural-agricultural UV 94.1 510850001 Petersburg VA 37.636 77.317 59 Rural-agricultural UV 98.9 511310001 Not in an MSA VA 37.290 75.972 12 Rural-agricultural UV 94.5 511611104 Roanoke VA 37.283 792884 346 Suburban-residential UV 98.0 540690007 Wheeling WV 40.121 80.700 212 Suburban-residential UV 94.6 550890005 Milwaukee WI 43.321 87.941 195 Suburban-residential UV 98.9 551171002 Sheboygan WI 43.669 87.740 202 Rural-agricultural UV 98.7 Subregion*/communality
1/0.29 

### 5/0.32 1/o.37 2/0.56 

### 4/0.40 4/0.46 2/0.48 

### 2/o.56 2/O.55 

### 4/0.35 4/o.44 4/0.38 

### 1/o.28 1/o.35 1/0.41 5/0.48 

### 5/O.46 2/0.52 2/0.61 2/0.27 2/0.42 3/0.36 

### 3/O.44 3/O.55 3/0.44 3/0.14 

### 5/o.48 5/0.50 5/0.42 5/0.50 4/0.42 4/0.58 4/0.64 

### 4/0.40 2/0.16 1/0.66 1/0.59 ~s

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