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Atmospheric Environment 41 (2007) 7127–7137

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The effects of meteorology on ozone in urban areas and their usein assessing ozone trends

Louise Camaliera,�, William Coxa, Pat Dolwickb

aOffice of Air Quality, Planning, and Standards, US Environmental Protection Agency, RTP, NC 27711, USAbOffice of Air Quality, Planning, and Standards, National Oceanic & Atmospheric Administration, RTP, NC 27711, USA

Received 28 February 2007; received in revised form 23 April 2007; accepted 25 April 2007

Abstract

The United States Environmental Protection Agency issues periodic reports that describe air quality trends in the US.

For some pollutants, such as ozone, both observed and meteorologically adjusted trends are displayed. This paper

describes an improved statistical methodology for meteorologically adjusting ozone trends as well as characterizes the

relationships between individual meteorological parameters and ozone. A generalized linear model that accommodates

the nonlinear effects of the meteorological variables was fit to data collected for 39 major eastern US urban areas. Overall,

the model performs very well, yielding R2 statistics as high as 0.80. The analysis confirms that ozone is generally increasing

with increasing temperature and decreasing with increasing relative humidity. Examination of the spatial gradients of these

responses show that the effect of temperature on ozone is most pronounced in the north while the opposite is true of

relative humidity. By including HYSPLIT-derived transport wind direction and distance in the model, it is shown that the

largest incremental impact of wind direction on ozone occurs along the periphery of the study domain, which encompasses

major NOx emission sources.

Published by Elsevier Ltd.

Keywords: Ozone trends; Generalized linear model; Meteorological adjustment; HYSPLIT; Spatial patterns

1. Introduction

The United States Environmental ProtectionAgency (US EPA) issues periodic reports thatdescribe the status and trends of air quality through-out the US (US Environmental Protection Agency,2005). Because inter-annual meteorological varia-tions are known to affect daily and seasonal averageozone concentrations, EPA often uses statisticaltechniques to reduce the effect that meteorological

e front matter Published by Elsevier Ltd.

mosenv.2007.04.061

ing author.

ess: Camalier.Louise@epa.gov (L. Camalier).

variations have on ozone trends (US EnvironmentalProtection Agency, 2005). Results from these peri-odic reports have typically been based on a relativelylimited suite of meteorological parameters. Thepurpose of this paper is to describe an improvedversion of the statistical model for meteorologicallyadjusting ozone trends that (1) accommodates thenon-linear effects of the meteorological variables, and(2) includes a much more comprehensive suite ofmeteorological variables than were used in previousanalyses (Cox and Chu, 1993, 1996).

The meteorological adjustment analysis focuseson urban areas located in the eastern US primarily

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because the number and spatial distribution ofurban areas is sufficient to identify geographicpatterns of the ozone response to meteorology.The initial analysis identifies the effect of eachmeteorological parameter on ozone at the 39selected eastern urban areas. Also examined isthe spatial distribution of these meteorologicaleffects on ozone, including the effects of tempera-ture, relative humidity and wind direction. The 39urban areas used in the analysis are a subset of 53metropolitan statistical areas (MSAs) that havebeen used in an EPA report (US EnvironmentalProtection Agency, 2004) on recent air qualitytrends.

2. Technical approach

There are numerous publications that describemethods for adjusting measured ozone for theeffects of meteorology (Bloomfield et al., 1996;Thompson et al., 2001; Davis et al., 1998). Aspreviously used in Zheng et al. (2006), this analysisemploys a generalized linear model (GLM) todescribe the relationship between urban ozone andselected meteorological parameters taken from anextensive array of candidate meteorological vari-ables. A separate model was fit for each urban areausing the GLM modeling function in the R softwareenvironment (R Development Core Team, 2006).The GLM can be written as follows:

gðmiÞ ¼ bo þ f 1ðxi;1Þ þ . . . f jðxi;jÞ þ . . . f pðxi;pÞk

þW d þ Y . ð1Þ

The subscript, i, indicates the ith day’s observa-tion, j, indicates the jth meteorological variable,where j ¼ 1,y, p, and the subscript, k, indicates thekth year. The parameter bo represents the overallmean and f ( ) is the smoothing function where fj

(xi,j) is the value of the smoothing functionassociated with the ith value of the explanatoryvariable j. The term, Wd, represents the effect of thedth day of the week, where d ¼ 1, 2, y, 7 (Sundayto Saturday, respectively). The term, Yk, representsthe effect of the kth year on ozone, i.e. themeteorologically adjusted value of ozone for thatyear, where k ¼ 1997, 1998, y, 2005.

The element, g (mi), represents the ‘‘link’’ function(McCullagh and Nelder, 1989), which specifies therelationship between the linear formulation on theright side of Eq. (1) and the expected response,the mi. Diagnostic evaluation of alternative link

functions indicates that a log link is the mostappropriate for these data. A natural spline (Hastieand Tibshirani, 1990) was employed to allow for anon-linear response between each meteorologicalparameter and ozone concentration. A naturalspline was also applied to a term used to accountfor seasonal changes.

As noted in the Introduction, previous analyses(Cox and Chu, 1993, 1996) have been confined to arelatively small array of meteorological parameters.To expand upon the suite of meteorological vari-ables that may have some impact on ozoneconcentrations, a more comprehensive data basehas been assembled by EPA containing an extensivearray of both hourly and daily meteorologicalparameters. The enhanced meteorological databaseconsists of nearly 700 meteorological sites across theUS and covers the period from 1995 to 2006. Theraw surface meteorological data are extracted fromthe integrated surface hourly (ISH) database whilethe raw upper air data is extracted from theIntegrated Global Radiosonde Archive (IGRA)databases, both of which are maintained byNational Climatic Data Center (NCDC). The sur-face data and upper air data are joined by pairingeach surface site with its nearest upper-air neighbor.The data pairing was only done for upper-air sitesconsidered to be ‘‘spatially representative’’ of thenearest surface site. A complete description ofthe omnibus data base along with a map showingthe locations of meteorological stations can befound on the scram website at: http://www.epa.gov/scram001/meteorology/omnibus_meteorological_data_set.pdf. The daily meteorological parametersconsidered in this analysis are shown in Table 1.Some of the variables are directly observed, whileothers are calculated based on hourly data or otherobserved parameters. In addition, ‘‘transport-related’’ variables were created based on the hybridsingle-particle Lagrangian integrated trajectory(HYSPLIT) trajectory model simulations (Draxlerand Hess, 1997). The HYSPLIT model was run foreach day of the data record to calculate 24-hbackward trajectories from each surface site. Thetrajectories were started at noon LST at a height of300m (i.e., within the mixed layer). Fig. 1 showsthe results of a single HYSPLIT trajectory forCleveland on 7 June 2005 and illustrates howtransport wind direction and transport distanceare determined.

The ozone air quality data used in the analysiswas taken from EPA’s air quality system (AQS)

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Table 1

List of daily meteorological parameters that comprise the

expanded meteorological data base and considered as part of

the adjustment model

Parameter

type

Parameter

Temperature

( 1C)

Maximum surface temperature

Morning and afternoon average temperature

Diurnal temperature change

Minimum, maximum, and average apparent

temperature

1200 UTC temperatures at 925, 850, 700, and

500mb

Deviation in temperature from a 10 year

monthly mean at 850, 700, and 500mb

24-h change in 1200 UTC 850mb temperatures

Wind (m s�1) Average daily u and v wind vectors

Average daily wind speed and direction

Morning and afternoon average u and v wind

vectors

Morning and afternoon average wind speed and

direction

Humidity Average daily relative humidity (%)

Midday and nighttime average relative humidity

(%)

Average and maximum dew point temperature

( 1C)

Maximum water vapor mixing ratio (g kg�1)

Morning 850mb dew point temperature ( 1C)

24-h change in 1200 UTC 850mb dew point

temperatures ( 1C)

Pressure (mb) Average station and sea-level pressure

Morning geo-potential height at 850, 700, and

500mb

Deviation in geo-potential height from a 10 year

monthly mean at 850, 700, and 500mb

Stability Difference in 1200 UTC temperatures between

surface and 850 ( 1C)

Maximum afternoon mixing height (m)

Maximum rate of mixing height increase

(mh�1)

Transport

trajectories

24-h HYSPLIT transport direction and distance

(1, km)

X, Y, and Z components of the 24-h HYSPLIT

trajectory

24-h scalar wind run (m)

Synoptic

weather

Average morning and afternoon fractional

cloud cover (%)

Total precipitation (in)

Binary indicators of the occurrence of rain,

haze, and fog

Fig. 1. HYSPLIT trajectory (red) diagram for the Cleveland

vicinity on 7 June 2005.

L. Camalier et al. / Atmospheric Environment 41 (2007) 7127–7137 7129

(http://www.epa.gov/ttn/airs/airsaqs/index.htm).For consistency with EPA’s ozone National Ambi-ent Air Quality Standards (NAAQS), the daily

maximum 8-h ozone concentration is extractedfrom AQS for each monitoring site within eachurban area. For analysis purposes, the highest 8-haverage among the monitoring stations in an urbanarea was selected to represent the ozone air qualityfor each day. The data included the months fromMay to September (i.e. the ozone season) for eachyear from 1997 to 2005. Finally, the modelingdatabase for each urban area was created bymerging the daily ozone data with the dailymeteorological data described above. Data fromthe meteorological station nearest to the center ofan urban area was chosen to represent themeteorology for that urban area. The resultingmodeling data base for each urban area was amatrix of approximately 1300 days (9 years times153 summer days) by 60 meteorological variables.

3. Model development

Standard, non-automated methods were used toidentify the most important meteorological vari-ables. The selection process included ‘‘backwardone variable deletion’’ based on the F-statistic(Venables and Ripley, 2002) along with diagnosticchecks such as the examination of model residuals.Variables that were highly correlated with oneanother and those which offered little explanatorypower are excluded early in the screening process(Harrell, 2001). The screening process is applied foreach urban area separately and examined forconsistency among all 39 areas. For example, dailymaximum 1-h temperature was statistically signifi-cant for 36 of the 39 urban areas and therefore was

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retained. The screening process resulted in a subsetof eight variables from the list of candidatemeteorological variables (Table 1) and this subsetwas then used in all subsequent modelings. Theeight variables used in the model are listed inTable 2. For each meteorological parameter, the‘‘Number of cities’’ column represents the numberof cites for which the parameter is statisticallysignificant at the a ¼ 0.001 level.

The predictive power of the model as measuredby the R2 statistic, ranges from 0.56 (Tampa) to0.80. Fig. 2 shows an interpolated surface of the R2

statistic for each urban area computed by using allof the predicted and observed daily maximum 8-hozone values from the 9-year period.

Table 2

Meteorological parameters used in the model and frequency of

significance

Meteorological parameters Number of cities

(out of 39)

Daily maximum temperature ( 1C) 36

Mid day average (10 am–4 pm average)

relative humidity (%)

37

Morning (7–10 am) average wind speed

(m s�1)

16

Afternoon (1–4 pm) average wind speed

(m s�1)

27

Morning surface temperature difference

(�1200 UTC) (temperature at

925mb–temperature at surface) ( 1C)

30

Deviation in 1200 UTC temperature of 850

mb surface from 10-year monthly average

( 1C)

17

Transport direction (degrees clockwise from

North)

37

Transport distance (km) 35

Fig. 2. Spatial Interpolation of the R2 statistic for predicted vs.

observed daily maximum 8-h ozone.

4. Meteorological effects on ozone

The parameter estimates obtained from themodel provide insight into the nature of theozone’s response to each meteorological variable.The individual effects of the most importantmeteorological parameters on ozone are exa-mined for an example area (Cleveland, OH),followed by an examination of the spatial distribu-tion of the ozone response among all 39 urbanareas. Next, the impact on ozone trends is exa-mined with a discussion of how inter-annualmeteorological variations affect the adjustmentprocess.

4.1. Individual meteorological effects on ozone

Each variable plays a unique role in explainingvariations in ozone through its own particularresponse, or effect. For example, increasing tem-perature is usually associated with increasing ozone,while increasing wind speed is usually associatedwith decreasing ozone (i.e. dilution effect). Becausemany of the meteorological effects are non-linear, apartial response plot offers an intuitive way toreveal the relationship between ozone and meteor-ological variables. A partial response curve showsthe effect of a particular meteorological variable onozone after accounting for the effects of all the othervariables. Thus, the partial response curve accountsfor any inter-correlation that may exist among theexplanatory variables. Fig. 3 displays the partialresponse of ozone to each of four meteorologicalvariables using data for the Cleveland urban area.Because a log-link function is used in the fittingprocess, the y-axis represents the log of ozoneconcentrations after adjusting for the overallaverage.

As visible in the upper-left quadrant in Fig. 3,the effect of temperature is relatively small fortemperatures below the threshold of �20 1C, butis very pronounced above that point. The individualresponse curves for relative humidity and transportdistance are decreasing and approximately log-linear over the range of data. Since transportdistance measures the distance traveled by the airmass within the 24-h period, larger transportdistances are associated with higher transport windspeeds, which act in the dilution of ozone andprecursor gases. As higher humidity levels areusually associated with greater cloud abundanceand atmospheric instability, the photochemical

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Fig. 3. Partial response of ozone to selected meteorological parameters—Cleveland, OH. Dashed lines are 95% confidence bounds for the

response.

Fig. 4. Percent change in ozone 1 1C�1 increase in daily

maximum 1-h temperature.

L. Camalier et al. / Atmospheric Environment 41 (2007) 7127–7137 7131

process is slowed and ground level ozone isdepleted. The ozone response to transport directionin Cleveland is nearly uni-modal. The modeindicates the direction from where the highestincremental impact on ozone occurs. In this case,the highest incremental impact on ozone is asso-ciated with transport winds originating in the southand southwest, while the lowest impact occurs fromwinds originating from the north. The 95%confidence bounds on each partial effect plot arevery narrow, indicating greater certainty in thepredicted response in the more dense portions ofthe data.

It is interesting to compare the sensitivity ofozone obtained from this particular study withsimilar sensitivity results obtained from studiesusing a numerical air quality model (Dawsonet al., 2007). As noted, an example given in theDawson article based on a July 2001 episodeindicates that daily maximum 8-h ozone concentra-tion in Atlanta increases by approximately 4 ppb2.5K�1 (�1.6 ppbK�1). This result comparesfavorably with the sensitivity to temperature foundfrom this study. For example, the ozone response totemperature in Atlanta is approximately 3.4%K�1

(Fig. 4). Since the seasonal average ozone in Atlantafor 2001 is approximately 60 ppb, the ozonesensitivity to temperature equates to �2 ppb in-crease in ozoneK�1.

4.2. Spatial distribution of meteorological effects on

ozone

Although each urban area is modeled indepen-dently, there are distinct spatial patterns in theresponse of each of the most important meteorolo-gical predictors. Since the ozone response for mostof the variables has at least some degree of non-linearity, presenting a spatial picture of the entirenon-linear response is difficult. As an alternative, alinear approximation of the effect of a particular

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Fig. 6. Percent change in ozone per 1% increase in mid-day

relative humidity.

Fig. 7. Effect of 24-h transport wind on ozone concentration.

L. Camalier et al. / Atmospheric Environment 41 (2007) 7127–71377132

meteorological variable on ozone is calculated usingthe central 50% of the data. The approximate lineareffect (i.e. ozone response) is defined as thedifference between the ozone predicted by the modelat the 75%-tile and the 25%-tile of the meteorolo-gical variable, divided by the difference betweenthese two percentiles. The approximate linear effectof maximum temperature on ozone is illustrated asa slope in Fig. 5. The effect, or slope, is thus anapproximate rate of change in ozone that ispredicted to occur as the maximum temperaturevaries from a typical low to typical high value. Theeffect can also be expressed as percent change inozone per a 1 1C increase in temperature (%change ¼ slope� 100).

Fig. 4 shows the spatial distribution of theapproximate linear effect of temperature on ozone,expressed as percent change in ozone 1 1C�1

increase in temperature. Generally, the temperatureeffect is positive and clearly has the largest impact inthe North and Northeast portions of the domain. Inthe vicinity of the Great Lakes, a 1 1C increase intemperature is associated with about a 4% increasein ozone. Urban areas along the eastern seaboardhave the largest ozone response to temperature(approximately 5%). The magnitude of the tem-perature effect gradually decreases southward anddiminishes to below 1% 1C�1 in the Gulf Coastregions. Fig. 6 shows the spatial distribution of theapproximate linear effect of relative humidity onozone. The relative humidity effect is largelynegative with greatest impact in the southern urbanareas and less pronounced for the more northern

Fig. 5. Schematic for the calculation of an approximate linear

effect of maximum temperature on ozone.

The arrows show the transport wind direction associated with the

largest incremental increase in ozone; the length of the arrow is

proportional to the increase in ozone concentration accompany-

ing the indicated direction.

urban areas. Differing spatial dependency of me-teorological effects could be due to the fact that themaximum temperature varies more in the northernareas as they experience summertime cold fronts,which typically do not reach further south.

The direction of transport can play an importantrole in the formation and movement of ozone. Tocharacterize the effects of the transport direction onozone, Fig. 7 is used to show the spatial pattern ofthe directional effects among the urban areas. Thearrows on the map in Fig. 7 indicate the direction oftransport wind associated with the largest incre-mental increase in ozone concentration; transportwinds indicate the direction from which the wind iscoming, as they are based on backward trajectories.

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Fig. 8. Partial response of ozone to transport wind direction for

Gulf Coast urban areas demonstrates the potential for multiple

directions of high impact.

L. Camalier et al. / Atmospheric Environment 41 (2007) 7127–7137 7133

The length of each arrow is proportional to themagnitude of the incremental increase (or effect).Referring back to the bottom-left quadrant of Fig. 3for Cleveland, the highest incremental ozone isassociated with transport winds originating fromthe southwest (�200–2501, clockwise from North).

Overall, the spatial pattern shows that urbanareas with the largest response to transport windsare located along the periphery of the domain, whileurban areas in the central portion of the domain areassociated with the smallest directional impact(Tennessee, Kentucky, Ohio and West Virginia). Ageneral explanation for this pattern is that thelargest ozone producing sources of NOx emissions(US Environmental Protection Agency, 2005) arelocated near the central portion of the eastern US,where air masses are more stagnant and individualtransport directions are less important. The peri-meter urban areas, including the NortheasternUS, upper Midwest, and Gulf Coast, appear toindicate at least some impact of transport fromthe central portion of the domain (CAIR ModelingAnalysis, http://www.epa.gov/cair/pdfs/finaltech02.pdf); an exception may be the southeast Texas andLouisiana Gulf Coast.

As noted previously, only the transport directionassociated with the largest incremental impact isindicated in Fig. 7. While the figure is convenient forconveying the overall pattern, the one arrowapproach can mask variations in directional impactespecially in urban areas that indicate impact frommore than one principal direction. For example,urban areas in the Gulf coast region, includingBeaumont, TX and Baton Rouge, LA, show a bi-directional impact of transport direction (Fig. 8).One possible reason for this is the existence andlocation of large sources of VOC emissions (e.g.,petrochemical processes around Houston andsouthern Louisiana). Finally, it is important to notethat individual ozone episodes may be driven bysource–receptor linkages that are not reflected in theaverage transport wind effect.

All variables selected to be in the model have atleast some impact on ozone. However, because themodel is fit separately for each urban area, each citycan have different prevailing meteorological para-meters which drive the ozone response. The twoprevailing meteorological parameters for each citydisplayed in Fig. 9 are those with the largestF-statistics. The map synthesizes the dominatingmeteorological parameters in each area, illustratingthe geographic zones of meteorological influence

(bands of color). The information conveyed inFig. 9 confirms the existence of spatially consistentpatterns in the aggregate effects of the meteorolo-gical drivers on ozone. As previously seen by thespatial effects maps, day-to-day variations intemperature play the most significant impact onozone in the north while day-to-day variations inrelative humidity have a more dominant effect onozone in the south. Both relative humidity andtemperature play comparable roles in the mid-portion of the eastern US. The outskirts/coasts ofthe extreme north and south are driven heavily bytransport parameters.

Other meteorological parameters are included inthe model, as well as a term to account for day ofweek effects. The spatial effects of these additionalparameters have been explored, and some interest-ing geographic patterns have been found. Forexample, some geographic patterns of day of weekeffects have been observed. In the northeastern USand the Great Lakes area, such as Detroit, week-ends seem to be associated with higher ozoneconcentrations while weekdays seem to be asso-ciated with lower ozone values. Interestingly, in thesoutheastern US area, the day of week effect onozone appears to be reversed, with weekdays beingmore associated with higher ozone concentrations.

4.3. Aggregate meteorological effects on ozone

trends

The original motivation for improving the statis-tical model described in this paper is to better

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Fig. 9. Geographic zones of dominating meteorological influence, based on the F statistics of the meteorological parameters.

Fig. 10. Observed vs. meteorologically adjusted ozone trends for

Cleveland, OH.

L. Camalier et al. / Atmospheric Environment 41 (2007) 7127–71377134

understand ambient ozone trends, especially inthose urban areas affected by the implementationof control programs (US Environmental ProtectionAgency, 2005). Fig. 10 illustrates the effect ofmeteorological adjustment on ozone concentrationsusing the Cleveland urban area as an example. Thevertical axis is the daily maximum 8-h ozone valuefor each ozone season between 1997 and 2005. Thedotted line represents the actual, observed concen-tration data, while the solid line connects themeteorologically adjusted seasonal ozone averages(i.e. the term, Yk, in Eq. (1)). The standard error foreach of the meteorologically adjusted averages is�2–3%.

The meteorologically adjusted ozone trend shownin Fig. 10 is generally smoother than the observedozone trend, showing less of the inter-annualvariability that is mainly caused by inter-annualfluctuations in meteorology. The direction andmagnitude of the adjustments in Fig. 10 can belargely explained by the direction and magnitude ofthe relative humidity and temperature anomaliesfrom year to year. To illustrate, Fig. 11 shows therelative humidity and temperature anomaly for 2004and 2005 for each urban area, where the anomaly iscalculated as the difference between the mean for agiven year’s ozone season and the 9-year ozoneseason average. The average temperature in 2004 isgenerally lower than the 9-year average, especiallyin the Midwest and Great Lakes regions. Tempera-ture anomalies in the following year contrast with2004, with 2005 temperatures warmer than the 9-year average.

Anomalies during the ozone season for relativehumidity in 2004 and 2005 are also noticeablydifferent. Average humidity values in 2004 are muchhigher than the 9-year average, especially in theMidwest and Northeast areas. The relative humidityanomaly in 2005 is in general, opposite and moreextreme than the 2004 anomaly, where averagehumidity in 2005 is much lower than the composite9-year average. The combined effect of higher-than-average temperature and lower-than-averagehumidity in 2005 contributes to the downwardadjustment in ozone in 2005. Similarly, the com-bined effect of lower-than-average temperature andhigher-than-average humidity in 2004 contributes tothe upward adjustment in ozone.

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Fig. 11. Temperature and relative humidity anomalies for the ozone seasons of 2004 and 2005. Each anomaly is measured as the difference

between the average for the given year and the 9-year average. Negative values are lower than average and positive values are higher than

average.

Fig. 12. Meteorological adjustment to ozone (%). Positive values indicate an upward adjustment and negative values indicate a downward

adjustment in ozone.

L. Camalier et al. / Atmospheric Environment 41 (2007) 7127–7137 7135

Because meteorological effects on ozone generallyoccur on a regional scale, adjustments within thesame general geographic area are expected to besimilar. To examine this issue, a yearly adjustmentpercentage is calculated for each urban area anddisplayed geographically (Fig. 12). The adjustmentpercentage is calculated via

adjustment% ¼Oadj �Oraw

Oraw� 100,

where Oadj is the meteorologically adjusted ozone(i.e. the year effect) and Oraw is the raw average

ozone. Positive values indicate an upward adjust-ment while negative values indicate a downwardadjustment. The adjustment values for 2004 werepredominately positive, which means that 2004seasonal ozone averages were adjusted upward inmost urban areas. Overall, the adjustments in 2004range from �4 to 8% throughout the central andnorth central portions of the domain. In contrast,adjustments in 2005 were mostly negative with thelargest adjustments in the Midwest and Great Lakesregion. The smallest downward adjustments aregenerally confined to the southeast.

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Fig. 13. Comparison of average ozone between 2004 and 2005 (top two quadrants). The bottom two quadrants show the percent

difference in ozone between 2005 and 2004, before and after meteorological adjustment. Since the standard error, obtained from

bootstrapping, is �2–3%, the resulting margin of error for percent change is �5 percentage points.

L. Camalier et al. / Atmospheric Environment 41 (2007) 7127–71377136

Fig. 13 illustrates the importance of usingmeteorological adjustment methods when trying tointerpret differences in air quality levels between 2years. The top two quadrants show the seasonalaverage of daily maximum 8-h ozone for 2004 and2005. Ozone concentrations in 2005 are much higherthan ozone concentrations in 2004. In 2004, ozonevalues higher than 60 ppb are confined to a smallregion; while in 2005, ozone values greater than 60ppb dominate most of the domain. The bottom twoquadrants show the percent difference between 2005and 2004 in average ozone, before and aftermeteorological adjustment. Percent differences inobserved ozone are most pronounced in the westernportion of the domain, where differences peakabove 20%. After adjusting for meteorology,percent differences between the 2 years are insignif-icant. To estimate the significance of the percentchanges shown in Fig. 13, a 3-day, non-overlapping,blocked bootstrap (Davison and Hinkley, 1999;Hastie and Tibshirani, 1990) is used to estimate thestandard error (�2–3 percentage points) of thepercent change in ozone. Only a few urban areashave adjusted changes which exceed twice thebootstrap estimated standard error. It can beconcluded that the increase in ozone concentrations

from 2004 to 2005 is driven mainly by meteorolo-gical differences between the two years and not dueto a fundamental shift in air quality.

5. Concluding remarks and future applications

In 2004, the National Research Council issued areport entitled Air Quality Management in the

United States (NRC, 2004), providing severalrecommendations regarding improved decision-making in the context of environmental health andair quality. One of the major recommendations is tobetter track air quality progress in order to enablemore informed evaluations of past and present airpolicy decisions. In order to track progress towardair quality goals, routine air quality trend analysesare needed to confirm if emission controls areindeed reducing pollutant concentrations.

Meteorological adjustment of ozone concentra-tions provides a method to examine the underlyingeffects of control programs apart from the randominter-annual effects of meteorology. The benefits ofaccounting for meteorological conditions whenexamining air quality trends have been demon-strated in numerous publications over the pastdecade. This paper expands on previous analyses

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through an improved regression method as well asthe inclusion of several new variables provided bythe HYSPLIT trajectory model.

The authors are beginning to explore the applica-tion of similar methods for quantifying the effects ofmeteorological conditions on PM2.5 and PM2.5

components, specifically sulfates, nitrates and or-ganic carbon. Such pollutants are much morecomplex than ozone and thus present uniquechallenges for statistical modeling and for gaininga more complete understanding of the effect ofmeteorology on PM2.5 concentrations.

There is a growing interest in understanding andquantifying the global effects of climate change onthe environment. Advances in statistical modeling ofthe relationship between air quality and meteorologyshould help corroborate the results being obtainedfrom numerical simulations which predict the re-sponse of air quality to changes in climate conditions.The knowledge gained by contrasting the simulatedand observed response to meteorology shouldprovide additional insight into the likely long-termeffects of climate change on air quality levels.

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