8-Hour, Part I Page 1 of 52 1/29/05

A Conceptual Model for Eight-Hour Ozone

Exceedances in Houston, Texas

Part I: Background Ozone Levels in Eastern Texas

John W. Nielsen-Gammon, James Tobin, Andrew McNeel, and Guohui Li

Center for Atmospheric Chemistry and the Environment

Texas A&M University

January 29, 2005

The preparation of this report is based on work supported by the State of Texas through a

Contract from the Houston Advanced Research Center, Texas Environmental Research

Consortium and the Texas Commission on Environmental Quality.

Contact information:

John W. Nielsen-Gammon

3150 TAMUS

Texas A&M University

College Station, TX 77843-3150

n-g@tamu.edu

979-862-2248
8-Hour, Part I Page 2 of 52 1/29/05

Table of Contents

Page 3 Executive Summary: Findings and Recommendations

Page 7 List of Figures

Page 10 Chapter 1: Introduction

Page 11 Chapter 2: Data and Methodology

Page 11 2a) Data

Page 12 2b) Method of Estimating Background Ozone

Page 19 Chapter 3: Ozone Climatology

Page 19 3a) Background Ozone Variability

Page 21 3b) Background Ozone Climatology

Page 23 3c) Background, Local Contribution, and Total Ozone

Page 25 3d) Meteorological Correlates with Ozone

Page 29 Chapter 4: Principal Component Analysis

Page 37 Chapter 5: Principal Components and Background Ozone

Page 37 5a) The Relationship Between Principal Components and Ozone

Page 41 5b) PC1 as a Predictor of Background Ozone

Page 45 5c) Lagged PC1 as a Predictor of Background Ozone

Page 51 Chapter 6: Results

Page 52 References
8-Hour, Part I Page 3 of 52 1/29/05

Executive Summary: Findings and Recommendations

Key findings and recommendations of general interest are in boldface. Page numbers

are shown in [brackets].

From Chapter 1:

1. As has been done by TCEQ, it is useful to consider the daily ozone levels in a

particular area as being the sum of two quantities: the background ozone and the

local contribution. (page 10)

2. The background ozone is defined as the ozone level that would be attained if there

were no local anthropogenic (or unusual biogenic) emissions of ozone precursors. (page

10)

3. The local contribution is the difference between the 8-hour maximum background

ozone and the 8-hour maximum actual ozone. (page 10)

From Chapter 2:

4. The lowest 8-hour maximum cannot be taken to be the background ozone level in

Dallas-Fort Worth (DFW) or Houston-Galveston-Brazoria (HGA) because the lowest

maximum is often found in the urban core or other sites affected by local emissions.

(page 13)

5. A particular limited set of stations, assumed to be measuring pristine ozone under

appropriate wind conditions, is used in DFW and HGA for estimating the background

ozone in those two areas. (page 13-14)

6. Individual stations within the limited set typically, but not always, record the

lowest 8-hour ozone when the wind blows from the station toward the metropolitan area.

(page 15-19)

7. Other regions had few monitors, so none were excluded. (page 15)
8-Hour, Part I Page 4 of 52 1/29/05

From Chapter 3:

8. Taking DFW as an example, there is year-to-year variability in the timing and

magnitude of peaks of background ozone, but its interannual variability is sufficiently

small that a multi-year average is an appropriate measure of typical conditions. (page 19)

9. Day-to-day variability of background ozone can be a factor of two or more,

especially during late summer and early fall. (page 20)

10. There is relatively little variability of background ozone during the winter and

during occasional periods in the early summer. (page 20)

11. The regular annual variation is a fundamental component of background

ozone. (page 20)

12. All regions of eastern Texas have the same basic pattern of annual variation

of background ozone. Background ozone is low in December but starts rising

steadily in mid-January. A secondary maximum of background ozone is reached in

mid-May, followed by a period with lower ozone values. The overall maximum in

each region occurs in August or September, followed by a decline through the fall.

(page 21-22)

13. Northeast Texas (NETX) appears to have the highest background ozone

concentrations through the year. (page 22)

14. Differences in background ozone between NETX and DFW suggest that, because

of sampling problems, absolute background ozone levels cannot be compared between

regions with many sensors and regions with few sensors. (page 22)

15. DFW reaches a relative minimum in background ozone around the end of June

and an absolute maximum in late August, while HGA reaches a relative minimum in

early July and an absolute maximum in mid-September. (page 23)

16. The decline in ozone during the summer is largest in the southern regions and

smallest in the northern regions. (page 23)

17. Background ozone levels as computed by the present method are suspiciously low

at Beaumont-Port Arthur, suggesting that the sampling network has local source issues.

(page 23)
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18. The local contribution at DFW makes up less than a third of the total 8-hour

ozome maximum on average days. (page 24)

19. The local contributions at DFW and HGA peak in July and August. (page 24-25)

20. The local contribution at HGA is generally a greater percentage of the total ozone

than at DFW and reaches an average value of 0.035 ppmv in August. (page 25)

21. The local contribution at HGA averages 0.010 ppmv greater than at DFW,

while the background at HGA averages 0.010 ppmv less than at DFW, leading to

similar mean 8-h ozone levels. (page 25)

22. Background ozone levels at HGA and DFW correlate most strongly with the wind

direction (and component from the north) on the previous day, with slightly lower but

still highly significant correlations on the same day and two days previous. (page 26)

23. Also contributing to background ozone are weak winds at HGA and a lack of

precipitation at DFW. (page 27)

24. The local contribution at DFW and HGA is positively correlated with high

temperatures, low wind speeds, and a lack of precipitation. (page 27)

25. On days without precipitation, the average 8-h maximum ozone in HGA in

early September exceeds the 8-h standard. Ozone in DFW comes close. (page 28)

From Chapter 4:

26. Principal Component Analysis (PCA) is useful for representing the large-scale

winds, which may be expected to strongly control background ozone, as a small number

of continuously-varying patterns. (page 30)

27. Mean ozone-season winds are from the southeast in the HGA area and from the

south in the DFW area, rotating clockwise around a center of high pressure in the

southeastern United States. (page 31)

28. The leading principal component (PC1), which explains the greatest amount of

variability in the wind pattern, represents winds from the southwest (if positive) or

northeast (if negative). It therefore is an indicator of the presence (or absence, if positive)

of transport from the central and eastern United States. (page 31)
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29. The second principal component (PC2) is positive with winds from the northwest.

(page 31-32)

30. The total wind on any given day is the sum of the mean wind and the daily

amplitudes of the various principal components. (page 33)

From Chapter 5:

31. The principal component most strongly correlated with background ozone is

PC1, followed by PC5 and PC2. (page 37)

32. The correlations are strongest with the wind pattern one day before the ozone

event. (page 37)

33. By itself, PC1 explains background ozone variations of about 0.017 ppmv at

DFW and over 0.020 at HGA. (page 38)

34. The mean value of PC1 declines steadily from spring to fall, consistent with

the expected increasing prevalence of continental transport in late summer and

early fall. (page 41)

35. The intraannual variation of PC1 explains the early fall background ozone peak,

but does not explain why spring experiences higher background ozone than early

summer. (page 42)

36. PC1 is much less variable in the summer, so days with PC1 less than –8

(favorable for high background ozone) are somewhat more common in spring than

summer. The summer sees extended periods of onshore transport. (page 42)

37. Even with a given wind pattern (PC1), background ozone levels are higher by

close to 0.010 ppmv in spring than in early fall. (page 43)

38. The PC1 values on two days together are a better predictor of background

ozone than a single-day PC1 value. This finding is consistent with the expectation

that extended transport from the continent is more favorable for high background

ozone than single days with favorable winds. (page 45)

39. Winds from the southwest carry high ozone if they were recently from the

northeast. (page 46)
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40. At given values of PC1 at zero and two-day leads, background ozone in HGA is

0.010 ppmv to 0.018 ppmv higher in spring than in summer. This suggests that the

relatively high background ozone in springtime high regardless of the day-to-day weather

patterns. (page 47)

41. Previous research suggests that high springtime ozone values are a consequence

of the high lifetime of ozone in spring combined with a wintertime buildup of NOx. (page

48-49)

42. DFW background ozone is less sensitive to details of transport than HGA

background ozone. (page 49)
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List of Figures

Figure 2.1: Scatterplot of u and v for all days in which station CAMS 31, located

north of DFW, experienced the lowest maximum 8-h ozone. (page 16)

Figure 2.2: Scatterplot of u and v for all days in which station CAMS 94, located

south of DFW, experienced the lowest maximum 8-h ozone. (page 17)

Figure 2.3: Scatterplot of u and v for all days in which station CAMS 78, located

north of HGA, experienced the lowest maximum 8-h ozone. (page 18)

Figure 2.4: Scatterplot of u and v for all days in which station CAMS 34, located

south of HGA, experienced the lowest maximum 8-h ozone. (page 19)

Figure 3.1: Chart of average background ozone by month in the DFW region, 1994-

2003. (page 20)

Figure 3.2: Chart of daily background ozone in the DFW region for years 1998 and

2000. (page 21)

Figure 3.3: Six-year average background ozone in various regions in eastern Texas.

Curves in this and later figures are smoothed with a 31-point running mean filter. (page

22)

Figure 3.4: Six-year average local contribution, background, and maximum total

ozone at DFW. (page 24)

Figure 3.5: Six-year average local contribution, background, and maximum total

ozone at HGA. (page 25)

Figure 3.6: Six-year average local contribution, background, and maximum total

ozone at HGA on days without rainfall. (page 27)

Figure 3.7: Six-year average local contribution, background, and maximum total

ozone at DFW on days without rainfall. (page 28)

Figure 4.1: Subset of EDAS domain used for principal component analysis (PCA),

showing the mean wind field during ozone season (April-October). The bottom wind

vector in this and other figures is 5 m/s long. (page 30)

Figure 4.2: The leading two principal components (PC1 and PC2), plotted with

amplitude equal to one standard deviation. (page 31)
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Figure 4.3: The next two principal components (PC3 and PC4), plotted as in Fig. 4.2.

(page 32)

Figure 4.4: Principal components PC5 and PC7, plotted as in Fig. 4.2. (page 33)

Figure 4.5: Wind pattern described by (left) PC1=-11, PC2=8 (PC1 = -1 standard

deviation and PC2 = +1 standard deviation) and (right) PC1=-11, PC2=-8. (page 34)

Figure 4.6: Wind pattern described by (left) PC1=11, PC2=8 and (right) PC1=11,

PC2=-8. (page 35)

Figure 4.7: Wind pattern described by (left) PC1=0, PC2=8 and (right) PC1=0,

PC2=-8. (page 36)

Figure 4.8: Wind pattern described by (left) PC1=-11, PC2=0 and (right) PC1=11,

PC2=0. (page 37)

Figure 5.1: Mean background ozone in DFW as a function of the PC1 coefficient

(with zero lead). (page 39)

Figure 5.2: Mean background ozone in HGA as a function of the PC1 coefficient.

(page 39)

Figure 5.3: Frequency histogram of PC1 coefficients. (page 40)

Figure 5.4: 31-point running mean of daily average PC1 values. (page 41)

Figure 5.5: Plot of PC1 values throughout the ozone season. (page 43)

Figure 5.6: Background ozone values in Houston, by season, as a function of PC1.

(page 44)

Figure 5.7: Background ozone values in Dallas-Fort Worth, by season, as a function

of PC1. (page 44)

Figure 5.8: Background ozone values as a function of PC1 with 1-day lead and PC1

with no lead. (page 45)

Figure 5.9: Background ozone values as a function of PC1 with 2-day lead and PC1

with no lead. (page 46)

Figure 5.10: The difference between the April-May background ozone and June-July

background ozone in HGA, as a function of PC1 with 2-day lead and PC1 with no lead.

(page 48)

Figure 5.11: Background ozone in DFW as a function of PC1 with one-day lead and

PC1 with zero lead. (page 50)
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1. Introduction

The new federal standard for ozone restricts the average concentration of ozone

over an eight-hour period to 0.08 parts per million (ppm). This standard must be

achieved by the three-year average of the fourth-highest daily maximum eight-hour

ozone concentration at any particular monitor. The two obvious differences are the

averaging period (longer) and the allowed concentration (smaller). In the previous

standard, the one-hour limit could be exceeded at a particular station no more than three

times in a three-year period. With the new eight-hour standard, based upon an average,

there is no single event that establishes the amount by which a metropolitan area exceeds

(or complies) with the standard.

One consequence of this new standard is that ozone must be understood on a

broader, interannual basis. One very bad year does not have as great an impact in the

eight-hour standard as with the one-hour standard. Indeed, ozone levels in a good year

are just as important as ozone levels in a bad year, because it is the average of the good

and bad years that determines possible violations of the standard.

The purpose of this two-part study is to develop a conceptual model for eighthour ozone exceedances in the Houston-Galveston (HGA) metropolitan area. For a

variety of reasons, it is useful to consider the daily ozone levels in a particular area as

being the sum of two quantities: the background ozone, defined as the ozone level which

would be attained if there were no local anthropogenic emissions of ozone precursors,

and the local contribution, defined as the difference between the 8-hour maximum

background ozone and the 8-hour maximum actual ozone. This approach, first applied in

Texas by the Texas Commission on Environmental Quality (TCEQ) has conceptual as

well as practical advantages. Conceptually, such an approach is useful for understanding

how meteorology contributes to high ozone levels, since the processes that lead to high

background levels differ in important ways from the processes that lead to high local

contributions. Practically, any control strategies must take into account the amount by
8-Hour, Part I Page 11 of 52 1/29/05

which ozone can be reduced by local controls versus the amount that can be expected to

be transported in from other areas and possibly be modified by remote controls.

This report, the first of two parts, considers the climatology and meteorology of

background ozone levels in eastern Texas. Although the ultimate goal of these reports is

to understand eight-hour ozone exceedances in particular metropolitan areas, background

ozone levels tend to rise and fall somewhat in tandem throughout eastern Texas. Thus,

the report will simultaneously consider background ozone from northeast Texas to

Corpus Christi, including the Dallas-Fort Worth (DFW) metropolitan area.

Section 2 describes the data sources and the method for estimating background

ozone and local contributions. Section 3 describes the ten-year climatology of eight-hour

(8-h) ozone in eastern Texas, with respect to background exceedances and local

contributions. Section 4 describes the principal component analysis (PCA) of transport

winds during ozone season (April-October) and relates the PCA components to observed

wind patterns. Section 5 describes the relationship between background ozone and wind

patterns using PCA. Section 6 summarizes the results.

2. Data and Methodology

2a) Data

The ozone data for this study was downloaded from the Environmental Protection

Agency’s (EPA) Air Quality System (AQS) database, accessed via the web site

http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm. The full period of

record of hourly ozone observations on the web site, from 1994 to 2003, was downloaded

in July 2004.

Additional preliminary ozone data was obtained from the TCEQ web site

http://www.tnrcc.state.tx.us/cgi-bin/monops/8hr_monthly for 2004.
8-Hour, Part I Page 12 of 52 1/29/05

The surface meteorological data from the TCEQ network was obtained from the

TCEQ web site, and surface meteorological data from the hourly weather observing

network was obtained from the National Climatic Data Center (NCDC). The period

covered by the surface data is January 1999 through June 2003.

The gridded meteorological analyses were created by the National Meteorological

Center (NMC) using the Eta Data Assimilation System (EDAS). The analyses were

obtained from an archive at the National Center for Atmospheric Research (NCAR) that

stores the EDAS analyses on a 40 km grid. The analyses were retrieved for the months of

April through October, 1998-2004 (the final two months of 2004 were not yet available

as of this writing).

2b) Method of Estimating Background Ozone

Bryan Lambeth and Pete Breitenbach at TCEQ have developed the concept of

background ozone as the amount of ozone that would be present without local emissions

sources. We find this approach useful and adopt it here.

A sophisticated approach to estimating background ozone is presently in use at

TCEQ. This approach involves determining the transport wind direction and selecting as

an appropriate background concentration the highest 8-h ozone concentration observed at

an appropriate rural site upwind of the metropolitan area of interest.

The TCEQ approach is inherently accurate but also somewhat subjective and

labor-intensive. A simpler approach would be to compute the highest 8-h ozone

concentration at each of the monitors in a region and assign as the background value the

lowest 8-h maximum ozone concentration observed at any sensor. Both approaches

assume that monitors are sufficiently broadly distributed about a metropolitan area that at

least one sensor will always be uninfluenced by local emissions.
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The simpler approach is attractive for its simplicity, but is subject to error when

monitors close to concentrated emissions sources experience depressed ozone levels

because of the high concentrations of young precursors. The problem is illustrated by

computing the percentage of observations at each monitor that represent the lowest 8-h

ozone maximum in the entire region. In DFW, the most common lowest ozone

occurrences are at the stations closest to and downstream of the Dallas urban core. In

HGA, the situation is somewhat more complex.

The station that is most likely to have the lowest 8-h maximum ozone in HGA is

CAMS 407 (Crawford), with a hit rate of 19.1%. The second most likely site is also an

urban one, CAMS 408 (Lang), with a hit rate of 17.7%. These two urban sites cannot be

measuring low background levels, because they are surrounded by monitors, most more

rural, which observed higher 8-h maximum ozone.

Many of the other common low ozone sites are near the perimeter of the

observing network. One might presume that the best background sites would be

Galveston (with a nearly pristine fetch from the Gulf of Mexico) and Conroe (under

northerly or northeasterly wind conditions). However, to the south, both CAMS 11

(Clute, 17.5%) and CAMS 10 (Texas City, 12.7%) are much more likely to have the

lowest ozone than CAMS 34 (Galveston, 1.9%). CAMS 10 is an unlikely background

site, since it is in the immediate vicinity of extensive petrochemical refining. CAMS 11

is more plausible, but it too is immediately downwind of a major plant under the

prevailing wind direction. So it appears that even the more rural monitors are subject to

local contamination of background ozone readings.

In order to use stations that truly sample background ozone values, we have

specified a limited set of stations from which background ozone levels can be estimated

in DFW and HGA (Table 1). In DFW, these stations are chosen because they are

reasonably distant from the core metropolitan areas and are distributed throughout the

range of compass directions relative to DFW. The most distant stations are not selected

because spatial variations of background ozone would cause a low bias in the background
8-Hour, Part I Page 14 of 52 1/29/05

ozone estimates, as some stations would experience ozone lower than what was

approaching DFW. In HGA, we have selected all of the perimeter stations except CAMS

10 and CAMS 11.

Table 1: Stations included in the estimate of background ozone levels in HGA and DFW.

EPA Number CAMS Description Period Used

Houston/Galveston

48-201-0066 410 Westhollow all

48-201-0051 409 Croquet all

48-201-0029 26 NW Harris Co. all

48-339-0078 78 Conroe Relocated all

48-339-0089 65 Conroe all

48-167-0014 34 Galveston all (installed 11/1/96)

48-039-1016 11 Clute through 10/31/96

Dallas/Fort Worth

48-113-0087 402 Dallas Executive

Airport

4/04-7/16/96; 10/1/96-

8/27/97

48-085-0005 31 Frisco all

48-397-0001 69 Rockwall Heath all

48-139-0015 94 Midlothian Tower 7/17/96-9/30-96;

8/28/97-present

48-257-0005 71 Kaufman all

48-439-1002 13 Fort Worth NW through 6/5/00

48-439-0075 75 Eagle Mtn. Lake since 6/6/00

48-251-0003 77 Cleburne all

The monitoring network was smaller during the early part of the 1994-2003

period, and with some network configurations there was no monitor in certain upstream

directions. In those cases, the best available upstream monitor was used, even if that
8-Hour, Part I Page 15 of 52 1/29/05

monitor may have been affected by local emissions. Thus, Clute is used in HGA until

Galveston was installed, Dallas Executive Airport is used in DFW when Midlothian

Tower was not installed, and Fort Worth NW is used in DFW until Eagle Mtn. Lake was

installed. Since 2000, no such compromises were necessary.

In the other five regions considered here, there were typically five or fewer

monitors. With this small number, it was necessary to include all stations in the search

for background ozone values.

Except where otherwise noted, the data analysis in the remainder of this report is

restricted to data from the period 1998-2003, when data coverage was best.

If the lowest 8-h maximum from the selected stations truly measures background

ozone, background levels at each station should almost exclusively occur when the wind

direction is from the station toward the city. Figures 2.1-2.4 show the occurrence of

background ozone at two selected stations in DFW and two in HGA as a function of the u

and v components of the wind. Station CAMS 31 (Fig. 2.1) is located north of the DFW

area. Although winds are rarely from the north, most background ozone occurrences at

that station are associated with winds from the north and northeast. CAMS 94, to the

south, is upstream for the prevailing wind direction, and most background occurrences

there correspond to a southeast wind. In the Houston area, CAMS 78, north of town,

experiences background values when the wind is from the north and northeast, while

CAMS 34, southeast of the city, is a common background site under southeast wind

conditions. The background ozone appears to be reasonably well-behaved: winds from

the north tend to favor stations located to the north, while winds from the south favor

stations located to the south.
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Figure 2.1: Scatterplot of u and v for all days in which station CAMS 31, located north of

DFW, experienced the lowest maximum 8-h ozone.

Wind Components for Background Ozone at CAMS 31

-4

-2

0

2

4

-6 -4 -2 0 2 4 6

u component

v component
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Figure 2.2: Scatterplot of u and v for all days in which station CAMS 94, located south of

DFW, experienced the lowest maximum 8-h ozone.

Wind Components for Background Ozone at CAMS 94

-4

-2

0

2

4

-6 -4 -2 0 2 4 6

u component

v component
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Figure 2.3: Scatterplot of u and v for all days in which station CAMS 78, located north of

HGA, experienced the lowest maximum 8-h ozone.

Wind Components for Background Ozone at CAMS 78

-4

-2

0

2

4

-6 -4 -2 0 2 4 6

u component (m/s)

v component (m/s)
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Figure 2.4: Scatterplot of u and v for all days in which station CAMS 34, located south of

HGA, experienced the lowest maximum 8-h ozone.

3. Ozone Climatology

3a) Background Ozone Variability

As an example, the monthly average background ozone at DFW from 1994 to

2003 is shown in Fig. 3.1. Certain climatological features are generally present, though

they do not necessarily appear every single year. For example, the highest background

ozone tends to occur in late summer, with an occasional secondary peak in late spring.

The decline in background ozone in the fall is rather abrupt almost every year. Certain

periods stand out, such as the spring of 1998 when ozone levels were unusually high or

the spring of 2000 when ozone levels were unusually low, but in general it appears that

background ozone variations on monthly timescales would be reasonably well

represented by conditions averaged over several years.

Wind Components for Background Ozone at CAMS 34

-4

-2

0

2

4

-6 -4 -2 0 2 4 6

u component (m/s)

v component (m/s)
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Figure 3.1: Chart of average background ozone by month in the DFW region, 1994-

2003.

The monthly averages do mask considerable day to day variability, particularly

during the warm season. For example, Fig. 3.2 shows the daily background ozone in

1998 and 2000 in DFW. Variations of background ozone concentration by a factor of

two on a daily to weekly time scale are common. The largest variability takes place in

late summer and early fall, while the smallest variability occurs during the winter. Both

series also exhibit a period in June in which the variability is unusually low.

The 1998 and 2000 background ozone seasons shown in Fig. 3.2 are perhaps the

most dissimilar of the ten years of data, especially in springtime. Certainly the daily

peaks in ozone in the two years occur in different months. Nonetheless, both years seem

to follow the same basic pattern of ozone variation. Collectively, Figs. 3.1 and 3.2 lend

support to the idea that the regular annual variation is a fundamental component of

background ozone.

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Month

Year

DFW Monthly Mean Background Ozone

0.055-0.06

0.05-0.055

0.045-0.05

0.04-0.045

0.035-0.04

0.03-0.035

0.025-0.03

0.02-0.025

0.015-0.02
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Figure 3.2: Chart of daily background ozone in the DFW region for years 1998 and

2000.

3b) Background Ozone Climatology

The annual variation of background ozone across eastern Texas is shown in Fig.

3.3. Seven regions are included: Dallas-Fort Worth (DFW), Tyler-Longview-Marshall

(NETX), Beaumont-Port Arthur (BPA), Austin (AUS), Houston-Galveston (HGA), San

Antonio (SAT), and Victoria-Corpus Christi (VCC).

All seven regions have the same basic pattern of annual variation of background

ozone. Background ozone is low in December but starts rising steadily in mid-January.

A secondary maximum of background ozone is reached in mid-May, followed by a

period with lower ozone values. The overall maximum in each region occurs in August

or September, followed by a decline through the fall.

DFW Daily Background Ozone

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

1 32 63 94 125 156 187 218 249 280 311 342

Day of Year

Ozone (ppm)

1998

2000
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Figure 3.3: Six-year average background ozone in various regions in eastern Texas.

Curves in this and later figures are smoothed with a 31-point running mean filter.

The region with the highest background ozone concentrations through the year

appears to be NETX. The DFW region is comparable in background ozone levels to

NETX during the summertime, but during the rest of the year DFW is lower than NETX

by about 6-8 ppb. We are aware of no meteorological or photochemical reason why

background ozone levels should be substantially lower in DFW than NETX during most

of the year, including some periods in which NETX is highest and DFW lowest

throughout eastern Texas. We conclude that these differences are artifacts caused by

applying the background ozone estimation technique to regions with widely varying

numbers of stations and levels of urbanization. Because of these sampling differences, it

is inappropriate to directly compare the background ozone magnitudes in heavilyinstrumented and less-instrumented regions simultaneously.

The two populous (well-sampled, but heavily urbanized) regions are DFW and

HGA. The two exhibit similar background ozone levels during the winter and early

Background Ozone, 1998-2003

0

0.01

0.02

0.03

0.04

0.05

0.06

101

116

131

215

302

317

401

416

501

516

531

615

630

715

730

814

829

913

928

1013

1028

1112

11271212

1227

date (monthday)

ozone (ppm)

DFW

NETX

BPA

AUS

HGA

SAT

VCC
8-Hour, Part I Page 23 of 52 1/29/05

spring, climbing from 0.02-0.025 ppm around the New Year to 0.04-0.045 ppm by midApril. Beyond mid-April, the two curves diverge. HGA begins a decline in background

ozone that bottoms out in early July at levels almost as low as those at the beginning of

the year, while DFW continues rising to mid-May and declines only to about 0.043 ppm

around the end of June. DFW then climbs to a peak of 0.055 ppm in late August, while

HGA does not reach its background peak of 0.044 ppm until mid-September. By midOctober DFW background ozone has fallen to HGA levels, and the two continue to

decline rapidly for the rest of the year.

The other five regions vary in a similar manner, with a continuous gradation in

the ozone climatology from northeast to southwest. The spring peak occurs latest at

NETX and earliest at VCC. The decline in ozone during the summer is largest in the

southern regions and smallest in the northern regions. The summer minimum of

background ozone occurs latest and is most pronounced in VCC and is earliest and least

pronounced in DFW and NETX. The minimum in mid-summer is so strong at VCC that

the background ozone levels in mid-summer there are considerably lower than the

background ozone levels in mid-winter. The late summer maximum is earliest at DFW

and NETX and latest at VCC.

The outlier from this pattern is BPA. At various times throughout the year,

particularly when background ozone levels are high, the BPA background levels are

much lower than would be expected from the trends in surrounding regions. Perhaps

background ozone at BPA is being scavenged by local sources near the monitors.

3c) Background, Local Contribution, and Total Ozone

If a small number of stations adversely affects the estimation of background

ozone, it must seriously harm the estimation of local contribution. Therefore, we only

consider the local contributions in DFW and HGA.
8-Hour, Part I Page 24 of 52 1/29/05

Figure 3.4: Six-year average local contribution, background, and maximum total ozone

at DFW.

The components of average maximum 8-h ozone at DFW are shown in Fig. 3.4.

The local contribution, on average, is a relatively insignificant component of DFW’s 8-h

maximum ozone, contributing less than a third of the total ozone. (The local contribution

might nevertheless be more significant on extreme ozone days.) The peak local

contribution occurs in mid-July, when temperatures are highest and solar radiation is

strong. Because the peak local contribution is nearly in phase with the early summer

minimum in background ozone, the 8-h maximum ozone does not have a pronounced

early summer minimum.

The components of average maximum 8-h ozone at HGA are shown in Fig. 3.5.

Unlike at DFW, a midsummer minimum is retained in the 8-h maximum ozone at HGA.

The background ozone levels in spring and fall are almost identical. The peak local

contribution in early August leads to the 8-h maxima being shifted closer to mid-summer

than the background maxima: the spring 8-h maximum is nearly a month later than the

DFW 8-h Ozone

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

101

128

224

323

419

516

612

709

805

901

928

1025

1121

1218

Date (monthday)

Ozone (ppm)

8-h Max

Background

Local Cont.
8-Hour, Part I Page 25 of 52 1/29/05

background ozone maximum, while the late summer 8-h maximum is nearly a month

earlier than the background peak.

Figure 3.5: Six-year average local contribution, background, and maximum total ozone

at HGA.

The local contribution is about 0.01 ppm larger at HGA than at DFW. Roughly

speaking, this makes up for background ozone values which during the summer are about

0.01 ppm larger at DFW than HGA, yielding comparable mean daily 8-h ozone maxima.

3d) Meteorological Correlates with Ozone

For an initial assessment of the meteorological and external factors that control

the variations in background ozone and local contributions, correlations were computed

HGA 8-h Ozone

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

101

128

224

323

419

516

612

709

805

901

928

1025

1121

1218

Date (monthday)

Ozone (ppm)

8-h Max

Background

Local Cont.
8-Hour, Part I Page 26 of 52 1/29/05

with respect to 58 possible predictors, involving wind, temperature, rainfall, day of week,

and day of year. The results are shown in Table 2.

Table 2: Strongest meteorological correlations with ozone. Meteorological parameters

ending with a 0, 1, or 2 correspond to the day of the ozone event, the day prior to the

ozone event, and two days prior to the ozone event, respectively. sinD and cosD are the

sine and cosine of the direction of the 24-h mean wind, u and v are the components of

wind toward the east and north, spd is the wind speed, isX is 1 if the wind is from the X

direction and 0 otherwise, binp (binpt) is 1 if there was measurable (measurable or a

trace) precipitation during the 24-hour period and 0 otherwise (or during the afternoon

six-hour period if indicated by “6h”), and tmp is the maximum temperature.

HGA

Background

DFW

Background

HGA

Local

DFW

Local

cosD1 .578 cosD1 .470 tmp0 .478 tmp0 .374

v1 -.538 v1 -.462 spd0 -.358 spd0 -.306

cosD2 .501 v2 -.433 binpt0 -.335 spd1 -.291

v0 -.483 cosD2 .428 binp0 -.320 isS1 -.247

cosD0 .469 binpt0 -.381 binp6ht0 -.314 isS0 -.228

v2 -.445 isS1 -.361 spd1 -.304 v1 -.220

isS0 -.391 binp0 -.351 u1 .276 isS2 -.198

isS1 -.391 v0 -.347 binp6h0 -.294 spd2 -.195

spd0 -.384 cosD0 .338 u2 .231 binp6ht0 -.195

spd1 -.372 tmp0 .336 u0 .219 binpt0 -.193

isNE1 .358 isS2 -.309 sinD2 -.200 binp0 -.187

isNE2 .315 binp6ht0 -.304 isSW1 .193 v2 -.186

The background ozone levels depend most strongly on the cosine of the previous

days’ wind direction and the north-south component of wind. For both two parameters, a

northerly wind (a wind from the north) favors high background ozone, with direction

slightly more important than speed. Northerly winds on other days are also favorable.
8-Hour, Part I Page 27 of 52 1/29/05

Also contributing to background ozone are weak winds at HGA and a lack of

precipitation at DFW.

The same-day temperature is most strongly correlated with local contributions to

ozone at both HGA and DFW, followed by wind speed, with weak winds favored.

Precipitation is also a negative influence, especially at HGA. At DFW a weak south

wind strongly favors high ozone, but at HGA the wind component from the west seems to

favor high ozone.

The importance of light winds for high local contributions is not surprising. The

strong dependence of background ozone on the north-south component of wind is not

particularly surprising either: a strong southerly wind is known to typically bring clear

maritime air to Texas.

Figure 3.6: Six-year average local contribution, background, and maximum total ozone

at HGA on days without rainfall.

HGA 8-h Ozone on Days Without Precipitation

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

1/1/00

2/1/00

3/1/00

4/1/00

5/1/00

6/1/00

7/1/00

8/1/00

9/1/00

10/1/00

11/1/00

12/1/00

8-h Max

Background

Local Cont.
8-Hour, Part I Page 28 of 52 1/29/05

In HGA, the elimination of precipitation days causes a dramatic increase in ozone

levels during late August and early September (Fig. 3.6). The late summer peak becomes

a sharp one, as background ozone is 0.01 ppm higher with rain days excluded. The 8-h

ozone maximum is almost entirely due to the background maximum. This peak is so

high that on days without rainfall, an average day in HGA in late August and early

September exceeds the 8-hour ozone standard.

In DFW, the elimination of precipitation days does not cause so dramatic an

increase in ozone levels, because precipitation days are less frequent in DFW than HGA

(Fig. 3.7).

Figure 3.7: Six-year average local contribution, background, and maximum total ozone

at DFW on days without rainfall.

Any conceptual understanding of background ozone levels in eastern Texas must

explain the late spring peak in background ozone, the midsummer minimum, and the late

summer maximum.

DFW 8-h Ozone on Days Without Precipitation

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

1/1/00

2/1/00

3/1/00

4/1/00

5/1/00

6/1/00

7/1/00

8/1/00

9/1/00

10/1/00

11/1/0012/1/00

8-h Max

Background

Local Cont.
8-Hour, Part I Page 29 of 52 1/29/05

4. Principal Component Analysis

Wind patterns and trajectories from the central United States are well known to be

associated with high levels of ozone in Texas. However, quantification of this

relationship is difficult. Previous investigators have used swarms of trajectories for

arbitrarily-defined high-ozone and low-ozone days in an attempt to determine preferred

directions of transport. However, this approach is not easily quantified and the proper

length of the trajectories is not clear.

An alternative approach would relate the local winds in the region to background

ozone levels. However, this approach would be sensitive to the correlation between the

local wind and the broader-scale wind pattern. In a sense, the local wind is being used as

a proxy for the larger-scale pattern.

The convenience of using the local wind is that the local wind can be described by

two variables: either the two wind components, or the wind speed and direction. A

complete description of a larger-scale wind field requires 2n variables, where n is the

number of grid points comprising the larger-scale wind field. The number of dimensions

quickly becomes intractible.

Yet another approach, called cluster analysis, seeks to find particular recurring

weather patterns and group the observed weather patterns into clusters. This approach is

appropriate when there are particular coherent patterns of variability, with the atmosphere

rapidly transitioning from one mode to another. But when the atmosphere varies

gradually and continuously, as it tends to during the summer in Texas, cluster analysis

produces an arbitrary grouping of weather events with little or no underlying physical

basis.

One purpose of principal component analysis, or PCA, is to reduce the

dimensionality of a large data set into a smaller, more manageable number. PCA finds
8-Hour, Part I Page 30 of 52 1/29/05

the dominant modes of variability of the weather patterns. If the day-to-day variations in

weather can be described by a very small number of principal components, or empirical

orthogonal functions (EOFs), the evolution of the weather can be easily represented on a

graph or set of graphs. Furthermore, it is likely that only a small subset of the principal

components will be strongly correlated with ozone levels, leading to even smaller

dimensionality.

Based on previous conceptual models and trajectory analysis, we define a subset

of the EDAS grid as shown in Fig. 4.1. The grid covers the area from eastern Texas to

the lower Mississippi River valley and extends as far north as extreme southwestern

Kentucky. The original EDAS grid (not shown) is sampled at every other grid point,

Figure 4.1: Subset of EDAS domain used for principal component analysis (PCA),

showing the mean wind field during ozone season (April-October). The bottom wind

vector in this and other figures is 5 m/s long.
8-Hour, Part I Page 31 of 52 1/29/05

yielding a grid of 11x16 points. The 925 mb winds (0.5 km to 1. 0 km above ground

level) were selected as the winds in the transport layer. PCA was then performed on the

1800 UTC (12:00 Noon CST) winds to extract the dominant patterns.

Fig. 4.1 shows the mean winds over the six years of ozone seasons. The wind

pattern is dominated by anticyclonic flow around a high pressure center to the east. Flow

enters Texas from the south and exits toward the north.

The dominant principal components are plotted in Figs. 4.2-4.4. Fig. 4.2 shows

that PC 1, which represents the strongest mode of variability, corresponds in its positive

phase to a vector oriented southwest to northeast, which, when added to the mean wind in

Fig. 4.1, produces flow from the south or southwest. Also shown in Fig. 4.2 is PC2,

Figure 4.2: The leading two principal components (PC1 and PC2), plotted with

amplitude equal to one standard deviation.
8-Hour, Part I Page 32 of 52 1/29/05

which in its positive phase is confluent flow from west to east and in its negative phase is

difluent flow from east to west.

Fig. 4.3 shows PC3 and PC4. PC3 is an anticylone over the domain when

positive and a cyclone when negative. PC4 is a nearly pure deformation field which

when positive corresponds to a front oriented northwest-southeast and when negative

corresponds to a front oriented northeast-southwest. Together, PC3 and PC4 combine to

represent the standard local patterns of nondivergent flow, while PC1 and PC2 can

combine to represent the larger-scale wind pattern.

Figure 4.3: The next two principal components (PC3 and PC4), plotted as in Fig. 4.2.

Fig. 4.4 shows the wind patterns for principal components PC5 and PC7. While

there is still a somewhat large-scale signal in these principal components, it is weak. The
8-Hour, Part I Page 33 of 52 1/29/05

only principal component beyond PC7 to be highly (negatively) correlated with ozone is

a wind pattern that corresponds to convergence in the domain of interest.

The wind pattern on each day can be exactly expressed as the sum of

2x11x16=352 principal components of varying amplitudes. More importantly, the wind

pattern can be approximated as the sum of a much smaller set of principal components of

varying amplitudes. In fact, the basic large-scale transport patterns can be recovered

from just principal components 1 and 2.

Figure 4.4: Principal components PC5 and PC7, plotted as in Fig. 4.2.

Actual wind patterns on particular days are equal to the mean wind plus the

principal component “loadings” for each principal component. Figures 4.5-4.8 show the

various wind patterns associated with +/- one standard deviation of various combinations

of PC1 and PC2. These plots include the mean wind and assume that all other principal
8-Hour, Part I Page 34 of 52 1/29/05

components are zero. Because of the orthogonality of the principal components, each of

the patterns in Figs. 4.7 and 4.8 are equally common, as are (albeit slightly less common)

each of the patterns in Figs. 4.5 and 4.6. Most common is the pattern in Fig. 4.1, which

consists solely of the mean. Collectively, these wind patterns include predominant wind

directions from the southwest, south, southeast, east, northeast, and north.

In the following section, ozone levels will be plotted as a function of PC1, or of

PC1 and PC2. The wind patterns provided in this section should be used as reference

material showing the two-dimensional wind field associated with particular values of

PC1 and PC2.

Figure 4.5: Wind pattern described by (left) PC1=-11, PC2=8 (PC1 = -1 standard

deviation and PC2 = +1 standard deviation) and (right) PC1=-11, PC2=-8.
8-Hour, Part I Page 35 of 52 1/29/05

Figure 4.6: Wind pattern described by (left) PC1=11, PC2=8 and (right) PC1=11,

PC2=-8.
8-Hour, Part I Page 36 of 52 1/29/05

Figure 4.7: Wind pattern described by (left) PC1=0, PC2=8 and (right) PC1=0, PC2=-

8.
8-Hour, Part I Page 37 of 52 1/29/05

Figure 4.8: Wind pattern described by (left) PC1=-11, PC2=0 and (right) PC1=11,

PC2=0.

5. Principal Components and Background Ozone

5a) The Relationship Between Principal Components and Ozone

The correlations and significance levels between the various principal

components at different lead times and background ozone levels in DFW and HGA are

given in Table 3. PC1 is found to be quite highly significant at all three tested lead times,

with the greatest significance at a lead of one day. Other principal components,

particularly PC5 and PC2, are significantly correlated with one or more ozone

parameters, but PC1 is both the dominant wind mode and the dominant controlling factor

for ozone.
8-Hour, Part I Page 38 of 52 1/29/05

Table 3: Correlations between principal components at 0, 1, and 2 day lead times with

respect to background ozone at Dallas and Houston. The correlations in bold are

significant at the 95% confidence level.

Despite the strong correlation, the relationship between background ozone and

PC1 is not a linear one. Figures 5.1 and 5.2 show the mean background ozone as a

function of the PC1 amplitude for DFW and HGA, respectively. In DFW, PC1 by itself

can explain variations of background ozone from 0.038 ppm to 0.054 ppm, comparable in

magnitude to the season variations of ozone. In HGA, the difference is even more

pronounced: positive values of PC1 are associated with ozone below 0.03 ppm, while

negative values approach or even exceed 0.05 ppm.

 HOUSTON - GALVESTON, 18Z WINDS AND BACKGROUND OZONE

SAME DAY 1 DAY LEAD 2 DAY LEAD

Correlation P-value Correlation P-value Correlation P-value

PC 1 -0.369 0.000 -0.417 0.000 -0.334 0.000

PC 2 -0.039 0.173 0.074 0.010 0.097 0.001

PC 3 0.131 0.000 0.071 0.014 0.004 0.879

PC 4 0.057 0.046 0.020 0.482 -0.003 0.931

PC 5 0.197 0.000 0.241 0.000 0.171 0.000

PC 6 0.024 0.408 -0.025 0.379 -0.042 0.152

PC 7 0.062 0.032 0.013 0.654 -0.009 0.756

PC 8 -0.035 0.224 -0.051 0.079 -0.087 0.003

PC 9 0.084 0.003 0.003 0.911 -0.065 0.026

PC 10 0.029 0.308 -0.051 0.078 -0.052 0.071

 DALLAS - FORT WORTH, 18Z WINDS AND BACKGROUND OZONE

SAME DAY 1 DAY LEAD 2 DAY LEAD

Correlation P-value Correlation P-value Correlation P-value

PC 1 -0.317 0.000 -0.353 0.000 -0.294 0.000

PC 2 0.085 0.003 0.207 0.000 0.215 0.000

PC 3 0.038 0.188 0.014 0.635 -0.016 0.592

PC 4 0.108 0.000 0.061 0.034 0.077 0.008

PC 5 0.199 0.000 0.217 0.000 0.169 0.000

PC 6 0.029 0.320 -0.035 0.227 -0.080 0.006

PC 7 0.117 0.000 0.080 0.005 0.008 0.772

PC 8 0.026 0.362 -0.015 0.614 -0.032 0.275

PC 9 0.098 0.001 0.053 0.068 -0.013 0.662

PC 10 0.002 0.936 -0.024 0.414 -0.046 0.112
8-Hour, Part I Page 39 of 52 1/29/05

Figure 5.1: Mean background ozone in DFW as a function of the PC1 coefficient (with

zero lead).

Figure 5.2: Mean background ozone in HGA as a function of the PC1 coefficient.

Mean DFW Background O3 vs PC1

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

-36

-33

-30

-27

-24

-21

-18

-15

-12

-9

-6

-3

0

3

6

9

12

15

18

21

24

2730

33

36

PC1

Mean Background O3 (ppm)

Mean HGA Background O3 vs PC1

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

-36

-33

-30

-27

-24-21

-18

-15

-12

-9

-6-3

0

3

6

9

12

15

18

21

24

27

30

33

36

PC1

Mean Background O3 (ppm)
8-Hour, Part I Page 40 of 52 1/29/05

In both DFW and HGA, the highest background ozone levels are found when the

value of PC1 is close to –1 standard deviations from zero (a value of +/- 11). A sense of

the wind field associated with that value of PC1 is given by examination of Fig. 4.5,

which shows PC1 at –1 standard deviation and PC2 and +/- 1 standard deviation. Most

events with PC1 at –1 standard deviation will lie between the two extremes given by Fig.

4.5a and Fig. 4.5b.

Other principal components (not shown) have comparatively little association

with background ozone levels.

Keep in mind when viewing this and other charts that the distribution of PC1

coefficients is normal and centered at 0 with a standard deviation of 11.1 (Fig. 5.3). Very

few cases occur at the tails of the distributions.

Figure 5.3: Frequency histogram of PC1 coefficients.

Probability of PC1 Scores

0

0.025

0.05

0.075

0.1

0.125

0.15

-36

-33

-30

-27

-24

-21

-18

-15

-12

-9-6

-3

0

3

6

9

12

15

18

21

24

27

30

33

36

PC1

Probability
8-Hour, Part I Page 41 of 52 1/29/05

5b) PC1 as a Predictor of Background Ozone

Is the observed yearly cycle of background ozone associated with seasonal

changes in the background wind? The standard explanation for the late summer

maximum of total ozone is a decrease in the intensity of the Bermuda High and a

resulting decrease in the strength of onshore flow. This explanation, while plausible,

does not on its surface explain the other two maxima and minima of the seasonal ozone

cycle.

The mean variation of PC1 through the ozone season is shown in Fig. 5.4. PC1 is

largest in April (implying, according to the right side of Fig. 4.8, moderate southwesterly

winds). The value of PC1 decreases almost linearly with time through the summer,

reaching zero (implying the wind pattern of Fig. 4.1) in July, and approaching –8

(implying the wind pattern of Fig. 4.8 (left)) by the end of September before again rising.

Figure 5.4: 31-point running mean of daily average PC1 values.

31 Day Running Average of PC1 During Ozone 

Season

-10

-8

-6

-4

-2

0

2

4

6

8

10

4/16

4/27

5/8

5/19

5/30

6/10

6/21

7/2

7/13

7/24

8/4

8/15

8/26

9/6

9/17

9/28

10/9

Date

PC1
8-Hour, Part I Page 42 of 52 1/29/05

The change of PC1 from midsummer values near zero to late summer values near

–8 appears to be adequate to explain the change in mean background ozone during this

period. In DFW, ozone increases by 0.01, and the change in mean DFW background as

PC1 changes from 0 to –8 is similar to this amount. In HGA, too, the ozone increase of

0.02 is comparable to the change in mean HGA background as PC1 changes from 0 to –8.

It is therefore tempting to conclude that the variation of ozone during the latter half of the

year is explained by changes in the value of PC1, that is, a weakening of the Bermuda

High.

This hypothesis is easily tested, and it turns out that while the hypothesis can

explain the increase in background ozone at the end of the summer, it cannot explain the

decrease in background ozone at the beginning of the summer.

The next hypothesis considers the fact that weather is much more variable in

spring in Texas than in the summer. If summertime PC1 values remain near 0 while

springtime PC1 values are highly variable and frequently are strongly negative, it would

be possible for average springtime background ozone to be higher than average

summertime background ozone.

The scatter of PC1 values through the year is shown in Fig. 5.5. The figure

confirms that the variability of the wind field is much smaller in summer than in the other

two seasons. However, PC1 dropping below –8 is nearly as common in spring as in the

summer. Thus, the PC1 day-to-day variability is sufficient to explain why summer might

be similar in background levels to spring, but not why it might be lower.

Suppose that there is an aspect of background ozone unrelated to regional

meteorological conditions? If so, a given meteorological situation (PC1 value) would

produce a different background ozone value in some seasons compared to others.
8-Hour, Part I Page 43 of 52 1/29/05

Figure 5.5: Plot of PC1 values throughout the ozone season.

To test this idea, Fig. 5.6 shows the mean background ozone variation in HoustonGalveston as a function of PC1, by month. For most of the range of the principal

component, the Apr-May background ozone concentrations are about 0.01 ppm larger

than in the other months. This elevated background ozone in spring is insufficent to

explain the late spring maximum.

A similar plot for Dallas-Fort Worth (Fig. 5.7) exhibits a similar seasonal cycle

but with less amplitude. The DFW plot in particular emphasizes the fact that the

dependence of high ozone on PC1 is stronger in summer and fall than in spring.

Observed PC1 Values vs. Date, Ozone Season

-40

-30

-20

-10

0

10

20

30

40

4/1 4/16 5/1 5/16 5/31 6/15 6/30 7/15 7/30 8/14 8/29 9/13 9/28 10/13 10/28

Date

PC1 coefficient

1998

1999

2000

2001

2002

2003
8-Hour, Part I Page 44 of 52 1/29/05

Figure 5.6: Background ozone values in Houston, by season, as a function of PC1.

Figure 5.7: Background ozone values in Dallas-Fort Worth, by season, as a function of

PC1.

Mean HGA Background O3 vs PC1, by Months

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

-36

-33

-30

-27

-24

-21

-18

-15

-12

-9

-6

-3

0

3

6

9

12

15

18

21

24

27

30

33

36

PC1

Mean Background O3 (ppm)

Apr-May

Jun-Jul

Aug-Sep

Mean DFW Background O3 vs PC1, by Months

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

-36-33-30

-27-24-21

-18

-15

-12

-9

-6-3

0

3

6

9

12

15

18

21

24

27

30

33

36

PC1

Mean Background O3 (ppm)

Apr-May

Jun-Jul

Aug-Sep
8-Hour, Part I Page 45 of 52 1/29/05

5c) Lagged PC1 as a Predictor of Background Ozone

One reason that ozone levels for a given PC1 value in Aug-Sep might be higher

than in Apr-May could be that the rapidly-changing weather patterns in Apr-May could

inhibit long-range transport of polluted air to Texas. To examine whether a favorable

PC1 on successive days leads to higher background ozone levels, Fig. 5.8 shows the

average background ozone at HGA as a function of zero-lead PC1 and one-day lead PC1.

Figure 5.8: Background ozone values as a function of PC1 with 1-day lead and PC1 with

no lead.

Compared to Fig. 5.2, there is more variability explained by the two parameters

(zero-lead PC1 and one-day lead PC1) than by zero-lead PC1 by itself. If both days have

positive PC1, the background ozone averages less than 0.03 ppm. If the previous day’s

PC1 was strongly negative (transport from the northeast), almost any zero-lead PC1 will

be associated with ozone greater than 0.04 ppm. -22.6 -14.125 -5.65 2.825 11.3 19.775

22.6

14.125

5.65

-2.825

-11.3

-19.775

0

0.01

0.02

0.03

0.04

0.05

0.06

PC1 1-day lead

PC1 zero lead

HGA Background Ozone

0.05-0.06

0.04-0.05

0.03-0.04

0.02-0.03

0.01-0.02

0-0.01
8-Hour, Part I Page 46 of 52 1/29/05

If two successive days of northeast flow are conducive to high background ozone,

what about three successive days? Assuming that the wind rarely, if ever, switches from

the northeast and back again on successive days, this question can be addressed with a

plot of background ozone as a function of two-day lead PC1 and zero-lead PC1 (Fig. 5.9).

The dependence of background ozone is even stronger than in Fig. 5.8, with larger areas

below 0.03 ppm and above 0.05 ppm. According to this plot, three successive days of

northeasterly transport lead to background ozone well above 0.05 ppm in HGA.

Figure 5.9: Background ozone values as a function of PC1 with 2-day lead and PC1 with

no lead.

Note in Fig. 5.9 that a strongly-negative 2-day lead PC1 value is favorable for

relatively high background ozone even if 0-day lead PC1 is strongly positive. Transport

winds from the northeast that later reverse to southwesterly carry high-background ozone

over Houston twice: once coming, and once going. This observation corroborates the -22.6 -14.125 -5.65 2.825 11.3 19.775

22.6

14.125

5.65

-2.825

-11.3

-19.775

0

0.01

0.02

0.03

0.04

0.05

0.06

PC1 2-day lead

PC1 0-day lead

Houston Background Ozone

0.05-0.06

0.04-0.05

0.03-0.04

0.02-0.03

0.01-0.02

0-0.01
8-Hour, Part I Page 47 of 52 1/29/05

statistical analysis that showed that the wind direction most important to background

ozone is observed on days prior to the background ozone level.

The predictive power of this chart is impressive, but variations of PC1 still do not

explain the springtime background ozone maximum. Heretofore, the difference between

PC1 values in April-May and in June-July has accounted for less than 0.01 ppm, too

small to explain the observed April-May maximum. But now that the two-day lead plot

(Fig. 5.9) predicts so much of the background ozone variability, we can use this

information to control for the effect of transport variations.

Figure 5.10 shows the difference between spring and summer background ozone

at various combinations of PC1 with a 2-day lead and PC1 with zero lead. Compared to

Fig. 5.9, the values are remarkably uniform. Furthermore, except for the rare events

along the margins of Fig. 5.10, any given combination of 2-day lead PC1 and 0-day lead

PC1 is associated with 0.010 to 0.018 ppmv more background ozone in April-May than

in June-July. Assuming that other principal components, which are known to have both

lower amplitude and lower correlation with background ozone, cannot account for this

difference, one can conclude that a given weather and transport pattern is associated with

about 0.012-0.015 ppmv higher background ozone in spring than in summer.

The cause of this spring surplus of background ozone cannot be photochemistry,

because the solar insolation is greater in June and July. Similarly, the cause cannot be

less cloudiness and precipitation, because April and May are as wet or wetter than June

and July. A similar springtime ozone maximum has been observed at rural stations and

in the free troposphere throughout the Northern Hemisphere (for a review, see Monks

2000), and the decreased sensitivity of background ozone to wind pattern in springtime is

consistent with a hemispheric-scale background ozone signal.
8-Hour, Part I Page 48 of 52 1/29/05

Figure 5.10: The difference between the April-May background ozone and June-July

background ozone in HGA, as a function of PC1 with 2-day lead and PC1 with no lead.

The causes of this hemispheric springtime maximum remain somewhat

controversial (Monks 2000; Vingarzan 2004). Other trace constituents show a maximum

in springtime, including constituents with a wholly stratospheric origin and constituents

with a wholly tropospheric origin. Thus, while troposphere-stratosphere exchange

appears to play a role, other chemical mechanisms, such as wintertime buildup of

pollutants followed by springtime photochemistry, must also be operating. Evaluation of

global chemistry models by Wang et al. (1998) and Yienger et al. (1999) attribute the

peak in rural areas to various combinations of transport from the stratosphere,

tropospheric transport of ozone (with a long spring lifetime) from pollution sources, and

photochemistry resulting from a wintertime buildup of NOx. Closer to home, global

model simulations show that the high springtime ozone over Bermuda is associated

almost entirely with transport from North America, despite positive correlations with -22.6 -14.125 -5.65 2.825 11.3 19.775

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14.125

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-0.02 -0.015 -0.01 -0.0050 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04

PC1 2-day lead

PC1 zero lead

HGA Background Ozone Difference (AprMay-JunJul)

0.035-0.04

0.03-0.035

0.025-0.03

0.02-0.025

0.015-0.02

0.01-0.015

0.005-0.01

0-0.005

-0.005-0

-0.01--0.005

-0.015--0.01

-0.02--0.015
8-Hour, Part I Page 49 of 52 1/29/05

stratospheric tracers (Li et al. 2002), while springtime tropospheric ozone over North

America north of 40N above 5 km has a much stronger stratospheric source (Wang et al.

2003). It is likely that the Bermuda measurements and analyses are representative of

conditions in the subtropical western Atlantic, the area of origin of air transported to

Texas from the southeast in springtime. Given the longer lifetime of tropospheric ozone

in the cool season, the potential contribution to springtime background ozone in Texas

from Asian sources should also be considered.

DFW has a similar dependence on background ozone, but with some crucial

differences (Fig. 5.11). First, the two-day lead plot is not shown here because the

dependence on two-day lead PC1 is much weaker than at HGA. Apparently, DFW’s

closeness to continental anthropogenic sources reduces the need for several days of

transport. Second, the area of high background ozone is broader, and the total variation

of background ozone across the diagram is smaller, than in the corresponding HGA plot

(Fig. 5.8). This means that DFW is less sensitive to the details of transport, making its

late summer background ozone maximum less sharp than at HGA. Also, elevated

background ozone levels are found in the vicinity of PC1 = 0 at zero and one-day leads,

while at HGA the PC1 components needed to be negative before significantly elevated

ozone was likely. This means that as the season progresses and PC1 becomes

increasingly negative, DFW and other northern areas will see an increase of background

ozone before HGA and more southern areas. This makes sense because winds from the

east-southeast, corresponding to slightly negative PC1, would advect continental air to

DFW but maritime air to HGA.
8-Hour, Part I Page 50 of 52 1/29/05

Figure 5.11: Background ozone in DFW as a function of PC1 with one-day lead and PC1

with zero lead.

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0.01

0.02

0.03

0.04

0.05

0.06

PC1 with 1-day lead

PC1 with zero 

lead

DFW Background Ozone

0.05-0.06

0.04-0.05

0.03-0.04

0.02-0.03

0.01-0.02

0-0.01
8-Hour, Part I Page 51 of 52 1/29/05

6. Results

The key results of this research are as follows:

(1) The intraseasonal variability in 8-h maximum ozone in eastern Texas is primarily

associated with background ozone. Local contributions tend to be highest during the

summer when background ozone reaches a local or global minimum.

(2) The late spring peak in 8-h maximum ozone in eastern Texas is primarily associated

with “tropospheric background” ozone. This ozone maximum has been observed at rural

sites elsewhere, and is associated with variations in the lifetime of ozone, the

concentrations of NOx, and enhanced transport from the stratosphere.

(3) The midsummer minimum in background ozone in eastern Texas leads to a minimum

in 8-h maximum ozone that is strongest to the south and barely noticeable to the north.

The primary cause of the summertime minimum is a decline in the tropospheric

background ozone. Although the relatively clean southerly flow is weakening during this

period, most air parcel paths remain maritime in nature.

(4) As an easterly or northeasterly wind becomes more and more frequent later in the

summer, background and total ozone in eastern Texas begin rising. This takes place first

in northern regions of eastern Texas where southeasterly winds bring dirty air, then in

more southern regions later in August as large-scale easterlies and northeasterlies

develop. Winds are also less steady than in the middle of summer, so continental

transport becomes increasingly frequent.

(5) The peak 8-h ozone concentrations in eastern Texas occur at a time when the local

contribution is still significant and the background ozone is increasing.
8-Hour, Part I Page 52 of 52 1/29/05

References

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analyses. J. Geophys. Res., 107, D22, 4611, doi:10.1029/2002JD002138.

Monks, P. S., 2000: A review of the observations and origins of the spring ozone

maximum. Atmos. Env., 34, 3545-3561.

Vingarzan, R., 2004: A review of surface ozone background levels and trends. Atmos.

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Wang, Y., D. J. Jacob, and J. A. Logan, 1998: Global simulation of tropospheric O3-

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doi:10.1029/2003JD003592.

Yienger, J. J., A. A. Klonecki, H. Levy II, W. J. Moxim, and G. R. Carmichael, 1999: An

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