Final Report, March 16, 2009 

Partially Satisfying Task 6.2 

PROPOSAL FOR GRANT ACTIVITIES UNDER THE GRANT UMBRELLA FROM TCEQ 

TO THE UNIVERSITY OF TEXAS AT AUSTIN 

Grant Activities No. 582-5-86245-FY08-01 

Effects of Meteorology on Pollutant Trends 

Tracking Number: (2008-20) 

Prepared by 

Dave Sullivan, Ph.D. 

The University of Texas at Austin 

Center for Energy and Environmental Resources 

Building 133, MC R7100, 10100 Burnet Rd., Austin, TX 78758-4445 

Phone: 512-471-7805, E-mail: sullivan231@mail.utexas.edu 

1. Background 

The primary goal of this project is to analyze trends in air pollutants in a manner that will allow 

an assessment of the effectiveness of emission controls. Pollutant concentrations are known to 

be random variables that are functions of weather and emissions. Furthermore, “weather” 

comprises local weather and meso- to synoptic-scale meteorology, which affects pollutant 

transport, and “emissions” comprises local emissions and distant upwind emissions. 

The first phase of this project involved collecting as much data as practical to extend trends from 

2007 to as far back in time as 1990, or as far back as practical, in all nonattainment and near 

nonattainment areas in the state of Texas. These nonattainment and near nonattainment areas 

include the Houston-Galveston-Brazoria area, the Dallas-Fort Worth area, the Beaumont-Port 

Arthur area, the Tyler-Longview-Marshall area, the San Antonio area, the Austin area, the 

Corpus Christi area, and the Victoria area. These data include one-hour ozone, eight-hour 

averaged ozone, nitrogen oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOX), nonmethane hydrocarbons (NMHC), carbon monoxide (CO), all air toxics data including benzene, 

all pertinent and available meteorological data including upper level temperature, sea-level 

pressure gradients, surface wind speed, surface wind direction, upper level wind speed, and 

upper level wind direction. 

The second phase involves data analysis for trends. Two general approaches are used. First, 

using a least squares linear regression with appropriate statistical tests, trends for pollutant data 

in all nonattainment and near nonattainment areas have been analyzed. Complicating this simple 

approach is the selection of the metrics to track. The report includes discussion of rationale for 

having selected specific time periods during the ozone season and hours of the day. Second, 

using more advanced methods, trends have been analyzed taking meteorology into account. For 

ozone, a generalized linear model (GLM) has been used to remove meteorological influences 

from the ozone trends. Less sophisticated efforts have been employed for primary pollutants. 

1
Correlation and factor analysis methods have been applied to determine the most important 

meteorological variables and factors to employ in trend adjustments. 

The vast majority of all analyses have been performed with SAS 9.1 software, although R, SPSS, 

and MS Excel have also been used to a small extent. The HYSPLIT desktop application has 

been used for back-trajectory generation and analysis. MS MapPoint 2004 and Golden Software 

Mapviewer 7 were use to create some maps. 

2. Outline 

The outline of this report is described below. In the electronic version of this document, titles 

are hot-linked to the appropriate section. 

1. Background

2. Outline of report

3. History of this project

4. Data acquisition

5. Selection of trend indicators

6. Ozone (O3) trends

6.1 O3 ordinary least squares trends

6.2 O3 meteorologically-adjusted trends

6.2.1 Houston-Galveston-Brazoria conceptual model input

6.2.2. Assessing transport affecting the Houston region using HYSPLIT backtrajectories alone

6.2.3 Factor analysis to assess air-shed delineations

6.2.4 GLM development

6.2.5 Results

7. Oxides of nitrogen (NOx) trends

7.1 NOx ordinary least squares trends

8. Carbon monoxide trends

8.1 CO ordinary least squares trends

9. Sulfur dioxide (SO2) trends

9.1 SO2 ordinary least squares trends

10. Hydrocarbon (HC) species trends (highly reactive volatile organic compounds 

(HRVOC), benzene, toluene, ethyl benzene, xylenes (BTEX) compounds, and significant 

alkane compounds)

2
10.1 HC ordinary least squares trends

10.2 HC meteorologically-adjusted trends

11. PM2.5 trends

12. Summary

3. History of This Project 

June 2008 Monthly Report 1 

• Acquisition of upper air dataset. 

• Assessing ozone monitoring site longevity. 

• Factor analysis to assess air shed delineations using data 1995-2007. 

Conclusions: 63 EPA Regions 6 ozone sites have at least 12 years of data ending in 2007. If 

sites moved a short distance and reassigned a new AQS identity have their data combined with 

the older site, then 81 sites representing 43 counties have at least 12 years of data ending in 2007. 

Using the long time series of data, a multivariate analysis of ozone across Region 6 during 

summers from 1995 – 2007 reveals six geographical region factors. 

An upper air meteorological dataset called METDAT was acquired. Several unit conversions 

were required on a year by year basis, as not all annual files had the same formats and data 

conventions. 

July 2008 Monthly Report 2 

• Assignments given to graduate student assistant. 

• Revised Factor analysis to assess air-shed delineations using data 2005 – 2007. 

• Some initial trend analysis. 

Conclusions: Using more recent data (2005 – 2007) allowing more ozone sites representing 71 

counties for analysis, a revised factor analysis for regional ozone patterns reveals seven regional 

factors. 

August 2008 Monthly Report 3 

• Detailed seasonality modeling for one site (AIRS=482010024, Houston Aldine). 

• Correlation analysis of ozone with surface upper air meteorological data. 

• EPA Region 6 trend analysis for ozone. 

Conclusions: Ordinary least squares (OLS) regression of the average daily maximum 8-hour 

ozone averages (oz8hrpk) at the Aldine CAMS 8 site versus time shows a statistically significant 

downward slope in this measure from 1990 through 2007. There is evidence of a possible step 

down in concentrations beginning in 2001. A variety of SAS tools for transforming the variable 

and measuring the trend, the magnitude of seasonal effects, and the nature of the residual time 

series, found to be autoregressive order 1 (AR(1)), were used. 

3
Correlations of the natural logarithm of oz8hrpk for 65 sites with 10 years of data with the 

METDAT variables reveals patterns of correlation of ozone with meteorological variables 

related to the seven factors derived in the July report. 

An ordinary least squares regression of late summer (August – September) mean oz8hrpk shows 

86 out of 97 sites show negative slopes, and Harris County sites have the largest decline. West 

Texas and New Mexico sites show positive trends. 

September 2008 Monthly Report 4 

• EPA trend analysis Web pages. 

• Synoptic scale winds and regional trends. 

Conclusions: An EPA Web site contains much of the information requested in this project, but 

with much less detail, at www.epa.gov/airtrends. 

A regional East Texas trend analysis based on counting monitor-days with oz8hrpk >= 75 parts 

per billion (ppb) concentration for August – September, 1997 – 2007, using a fixed set of 

monitors with continuous data shows a significant downward trend. Severity of the ozone levels 

for August and September by year was qualitatively shown to be associated with the frequency 

of synoptic scales winds from northeast or from stagnant local wind patterns. 

October 2008 Monthly Report 5 

• EPA trend analysis Web pages and new ozone trend results. 

• Some early generalized liner model (GLM) regression results. 

Conclusions: A problem with the EPA trends at www.epa.gov/airtrends was identified. The 

research did not account for the changes in the number of ozone monitoring sites in metropolitan 

areas over time, which TCEQ has found to be a significant factor in trends. Using the core based 

statistical area (CBSA) geographic classifications used by EPA, a comparison of monthly 

average daily area maximum oz8hrpk using all sites versus only using sites with long-running 

data shows the selection of sites affects results in ordinary least squares regressions. 

Using SAS generalized linear model (GLM) procedure to regress ln(oz8hrpk) from Aldine 

CAMS 8 on METDAT data from Bush International Airport shows a statistically significant 

relationship with a “pseudo-R2

” value of 81.6 percent. 

November 2008 Monthly Report 6 

• Discussion of choices in SAS for trend approaches 

• Data quality assurance and HYSPLIT back-trajectories 

Conclusions: It is not a trivial task converting the R code used by EPA researchers for metadjusted trends to SAS code. 

Missing values for some HYSPLIT back trajectory variables in METDAT were coded as “999” 

and this may have affected some earlier correlation and regression results, but this was found to 

have been only a minor issue. 

4
December 2008 Draft Final Report and January 2009 Revised Draft Final Report 

• Data acquisition summary 

• Selection of trend indicators 

• Revised O3 ordinary least squares trends 

• O3 meteorologically-adjusted trends (correlation analysis) 

• Revised NO, NO2, NOx ordinary least squares trends 

• CO ordinary least squares trends 

• SO2 ordinary least squares trends 

• Revised hydrocarbon (HC) species trends 

• Initial discussion of meteorologically-adjusted trends for hydrocarbons (correlation 

analysis) 

Conclusions: Combining data from several urban sites with similar diurnal pattern for oxides of 

nitrogen (NOx), carbon monoxide (CO), and hydrocarbons (HC) led to the selection of hours 5 – 

8 CST (5:00 – 8:59 am CST) to average measurements for a variable to represent a daily variable 

for these parameters. The study of diurnal patterns for sulfur dioxide (SO2) was less definitive, 

so the daily one-hour maximum (so21hrpk) and daily average (so21hrav) were selected to 

represent daily variable. The daily variables were averaged across each year for each monitor 

with at least 10 years of data between 1990 and 2007 (1995-2007 for HCs). The independent 

variable in the OLS analysis was the number of years since 1990 (x = 0 ... 17). 

• All Houston area sites except Galveston CAMS 34 have negative slopes for NO, NO2 and 

NOx. Of the 24 regressions on NOx, 17 were significant at p=0.05. Results from nonHouston area sites were graphed, but were not summarized for statistical significance at 

this point. 

• For CO, of 17 sites around the state, only the Deer Park site is not statistically significant 

for a downward trend at a 0.05 level, but it is close (p=0.06). 

• For SO2, for 16 sites across the state, two variables per site, all had downward slopes, and 

24 of 32 trends were significant at p=0.05 for downward trends. 

• For HCs, the large majority of variables have downward slopes at the four sites with 

auto-GC data, but these were not summarized for statistical significance at this point. 

5
6

 

4. Data Acquisition 

An instance of the MOTHER data mart is in our possession. MOTHER includes surface 

meteorological and pollution data from 1972 – 2007 for EPA Region 6 states. We also have 

received a set of files from Mr. Erik Gribbin containing SAS data sets of aggregated national 

meteorological data from 1995 – 2007 from the Omnibus Meteorological Database1 (METDAT). 

These data sets contain surface and upper air data, including summarized 24-hour HYSPLIT2

back-trajectories. 

The METDAT data come in files for individual calendar years. The data were provided in two 

different formats, and the files have been combined in a uniform format and filtered to include 

data for monitoring sites in EPA Region 6. SAS code is provided in Appendix 2 and as a 

separate electronic attachment (metdat.sas). 

The METDAT SAS code sets up the data in three temporary files for manageability. The data 

for 2005 - 2007 are a slightly different format from earlier years’ data. These three more recent 

yearly files 

• contain quality control (QC) data, 

• use different variable names for mixing height rate of change and for water vapor level, 

• have different "date" information and formats, and 

• have temperature values in K degrees instead of F. 

The data are standardized by removing QC data, converting K to F, and by reformatting the date 

variable in the 2005-2007 data files, and by renaming two meteorological variables and dropping 

some unused date variables in the 1995-2004 data files. 

The data set provided by Mr. Gribbin also includes location data for meteorological monitors. 

The data describing the monitoring locations within EPA Region 6 is imported in the SAS 

program and the key location data are merged with the combined meteorological data file. The 

resulting file can now be merged with a dataset containing ambient pollution data based on date 

and proximity (using a nearest neighbor method described below). 

1

 See http://www.epa.gov/scram001/meteorology/omnibus_meteorological_data_set.pdf. Accessed March 2009. 2

 Draxler, R.R. and Rolph, G.D., 2003. HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Model 

access via NOAA ARL READY Website (http://www.arl.noaa.gov/ready/hysplit4.html). NOAA Air Resources 

Laboratory, Silver Spring, MD. 

Rolph, G.D., 2003. Real-time Environmental Applications and Display sYstem (READY) Website 

(http://www.arl.noaa.gov/ready/hysplit4.html). NOAA Air Resources Laboratory, Silver Spring, MD. 
Table 4.1 Types of METDAT data elements 

Morning and Afternoon average fractional cloud cover (tenths) 

Average and maximum dew point temperature 

Morning (~1200 UTC) 925 mb-surface temperature difference 

Morning Height of 500 mb and 700mb surface (m) 

Maximum mixing height (m) 

Maximum water vapor mixing ratio 

Rain, haze, relative humidity 

Morning temperature at 700, 850, 925 mb 

Average, minimum, maximum apparent temperature 

Max temperature, morning, afternoon temperature, delta 

temperature 

Transport distance, direction, u,v-components 

Morning, afternoon, daily wind speed, direction, u,v -components 

5. Selection of trend indicators 

In Camalier et al.3 the trend indicator for ozone is the annual average of the daily maximum 

urban-area eight-hour maxima. In our approach months May through October are used for 

meteorologically adjusted trends, in contrast to Camalier et al.’s use of May through September. 

Ozone issues in Texas persist later in the year than in northern states. In order to show the 

seasonality of ozone, ordinary least squares regression trends were run both on daily data, which 

have auto-correlation issues that degrade the quality of statistical tests, and on annual means. 

For NOx, CO, and hydrocarbons, the median diurnal patterns of the species data were examined 

to select a three or four hour period in the morning with the highest median concentrations. For 

NOx, trends were run for nitric oxide (NO), nitrogen dioxide (NO2), and total oxides of nitrogen 

(NOx). For hydrocarbons, subsets were used from the following classes: highly reactive volatile 

organic species (ethylene, propylene, 1-butene), aromatics (benzene, toluene, ethyl benzene, oxylene, m/p-xylene), and prominent alkanes (n-butane, isobutene, n-pentane, iso-pentane). For 

Sulfur dioxide (SO2), an examination of diurnal patterns across the state failed to reveal a 

common diurnal pattern as was the case with other pollutants. SO2 is principally a pollutant 

associated with coal burning, and in industrial areas to fugitive emissions from the sulfur 

removal process from feed stocks. Because of its lack of regular diurnal pattern, SO2 trends were 

run on both daily 1-hour maxima and daily 24-hour averages. 

A series of 72-hour HYSPLIT back-trajectories have been run from central Houston for all days 

with available data from May through October 1999 – 2007. The starting time was noon CST 

and from an altitude of 300 meters. A successful cluster analysis using HYSPLIT has also been 

run, showing six clusters. A preliminary examination shows the clusters are very similar to those 

run for two previous projects, centered in central eastern Texas. This corroboration suggests a 

robustness in the cluster analysis method over the East Texas area. These clusters have been 

 

3 Camalier, L., Cox, W., Dolwick, P., 2007. The effects of meteorology on ozone in urban areas and their use in assessing ozone 

trends. Atmospheric Environment 41, 7127-7137.

7
8

separately assessed as one form of meteorologically adjusted trend for ozone in support of 

Conceptual Model development in support of the SIP update. 
6. Ozone Trends 

6.1 Ordinary Least Squares Trends for Ozone 

Below in Figures 6.1 – 6.8 we present comparisons between trends based on the 30-day moving 

average of daily ozone eight-hour maxima by CBSA using only the subset of monitors with 

nearly-continuous data during the period of study. The moving average time series are not 

appropriate for ordinary least squares (OLS) regression owing to the clear periodicity in the data 

and the high level of auto-correlations between observations. Nevertheless we have fit linear 

regression lines to the data for the purpose of showing simple qualitative trends plus the seasonal 

patterns. Below each graph we list the AQS site numbers used in the regression and the p-value 

for the regression, which may only serve as a rough guide for statistical significance. Special 

attention was given to only use monitors that had nearly continuous data collection over the 1990 

– 2007 period. The importance of taking this approach rather than using all available monitoring 

data is that we have shown in the past that the number of monitors active in a monitoring 

network affects the extreme value statistics such as maximum observed value and number of 

ozone exceedance days.4

 

Figure 6.1 Austin 8-hr max 30-day moving average with regression line using only long-running sites’ data 

Sites used: 484530014; p = 0.22 

 

4

 D.W. Sullivan and Z. Fang, “Design Value as a Preferred Indicator over Exceedance Day Counts,” TCEQ Trade 

Fair, 2004. 

9
Figure 6.2 BPA 8-hr max 30-day moving average with regression line using only long-running sites’ data 

Sites used: 482450009, 482450011, 483611001; p < 0.0001

Figure 6.3 DFW 8-hr max 30-day moving average with regression line using only long-running sites’ data 

Sites used: 480850005, 481130069, 481130075, 484391002, 484392003; p = 0.64 

10
Figure 6.4 El Paso 8-hr max 30-day moving average with regression line using only long-running sites’ data 

Sites used: 481410037, 481410044, 481410055; p < 0.0001 

Figure 6.5 HGB 8-hr max 30-day moving average with regression line using only long-running sites’ data 

Sites used: 480391016, 482010024, 482010029, 482010046, 482010047, 482010051, 

482010062, 482010075, 482011034, 482011035, 482011039; p < 0.0001 

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Figure 6.6 SAN 8-hr max 30-day moving average with regression line using only long-running sites’ data 

Sites used: 480290032, 480290052; p < 0.0001 

Figure 6.7 CC 8-hr max 30-day moving average with regression line using only long-running sites’ data 

Sites used: 483550025, 483550026; p < 0.0001 

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Figure 6.8 CC 8-hr max 30-day moving average with regression line using only long-running sites’ data 

Sites used 481810001; p = 0.0004 

13
1. SAS code to determine long-running ozone sites and extend time series by merging nearby “early” and “

(filename get ozone longterm data.sas: libname cams2 "D:\cams2"; run; libname other "C:\Documents and Settings\Sully\My Documents\mother\OTHERDATA"; run; * This program prepares data for merger with the meteorological data sets provided by Erik * Read in all the data from 1990-2007 from MOTHER;* An earlier assessment (shown at bottom of program and using Excel) suggested that  time series for some monitors could be concatenated because of close proximity  and one having ended shortly before the other began (i.e., the site moved).;data ozone_sites0; 

set cams2.oz_1990 cams2.oz_1991 cams2.oz_1992 cams2.oz_1993 cams2.oz_1994 

 cams2.oz_1995 cams2.oz_1996 cams2.oz_1997 cams2.oz_1998 cams2.oz_1999  cams2.oz_2000 cams2.oz_2001 cams2.oz_2002 cams2.oz_2003 cams2.oz_2004  cams2.oz_2005z cams2.oz_2006 cams2.oz_2007; 

if airs = "481210033" then airs = "481210034" ; if airs = "481130045" then airs = "481130075" ; 

if airs = "483390089" then airs = "483390078" ; if airs = "481410028" then airs = "481410055" ; if airs = "401430127" then airs = "401431127" ; if airs = "482011003" then airs = "482011039" ; if airs = "481670014" then airs = "481671034" ; if airs = "051191005" then airs = "051191008" ; if airs = "482011037" then airs = "482010075" ; if airs = "350431002" then airs = "350431003" ; if airs = "482011041" then airs = "482011015" ; if airs = "484230004" then airs = "484230007" ; if airs = "220470002" then airs = "220470012" ; if airs = "350011003" then airs = "350010029" ; if airs = "220730002" then airs = "220730004" ; if airs = "350131012" then airs = "350130023" ; if airs = "482450102" then airs = "482451035" ; if airs = "350010028" then airs = "350010024" ; if airs = "220570002" then airs = "220570004" ; if airs = "484390057" then airs = "484393011" ; if airs = "220870002" then airs = "220870009" ; if airs = "480391003" then airs = "480391016" ; if airs = "400870073" then airs = "400871073" ; 

14
if airs = "400310647" then airs = "400310649" ; 

if airs = "480290036" then airs = "480290052" ; 

/* if airs in ("481210034" "481130075" "483390078" "481410055" "401431127" "482011039" "4/* "051191008" "482010075" "350431003" "482011015" "484230007" "220470012" "3/* "220730004" "350130023" "482451035" "350010024" "220570004" "484393011" "2/* "480391016" "400871073" "400310649" "480290052") ;*/

keep airs date oz8hrpk oz1hrpk oz1hrav oz1hrvh oz1hrvd oz8hrvd; 

run; * Figure out the length of the time series for each site;proc sort data=ozone_sites0; by airs date; run; data ozone_sites; set ozone_sites0; by airs date; if first.airs or last.airs; run; proc sort data=ozone_sites; by airs date; run; proc transpose data=ozone_sites out=ozone_sites; by airs; var date; run; data ozone_sites; 

set ozone_sites; 

 early = col1; late = col2; drop col1 col2; format early late mmddyy8.;  duration = (late - early)/365.25; run; * Get geographic data for sites, including 9 not in MOTHER;proc import datafile="C:\Documents and Settings\Sully\Desktop\Kasey's O3 trend project\9_o

out=nine_sites dbms=XLS; run; 

data site_info; 

set other.siteinfo_newx2 nine_sites; if site_description = "" then do; 

if city ^= "" then

 site_description = substr(city,1,8)||" "||substr(county,1,8)||" "||substr(site_addre

else

 site_description = substr(county,1,8)||" "||substr(site_address,1,8); 

end; 

run; proc sort data=site_info; by airs; run; data ozone_sites; 

merge ozone_sites (in=in_o3) site_info; by airs; if in_o3; keep airs early late duration latitude longitude; 

run; 

15
/*proc export data=ozone_sites outfile="C:\Documents and Settings\Sully\Desktop\190_o3_sit

replace; run;*/* which sites have > 15 years of data?;data ozone_sites_long; 

set ozone_sites; if duration >= 15; 

run; ******************************************************************************* 2. Compute moving averages for daily maxima ozone by urban area (CBSA) (filename sites over time2.sas) libname other "C:\Documents and Settings\Sully\My Documents\mother\OTHERDATA"; libname cams2 "D:\cams2"; data oz; 

set cams2.oz_1990 cams2.oz_1991 cams2.oz_1992 cams2.oz_1993 cams2.oz_1994 

 cams2.oz_1995 cams2.oz_1996 cams2.oz_1997 cams2.oz_1998 cams2.oz_1999  cams2.oz_2000 cams2.oz_2001 cams2.oz_2002 cams2.oz_2003 cams2.oz_2004  cams2.oz_2005z cams2.oz_2006 cams2.oz_2007; 

keep airs date oz8hrpk; 

run; proc sort data=oz; by airs; run; proc sort data=ozone_sites_long; by airs; run; data oz; 

merge oz ozone_sites_long (in=inlong); by airs; if inlong; 

run; data site_cbsa; 

set other.Siteinfo_newx2; keep airs cbsa_code cbsa_title state; 

run; proc sort data=site_cbsa; by airs; run; data oz_cbsa; 

16
merge site_cbsa oz (in=inoz); 

by airs; if inoz; if cbsa_code; 

run; proc sort data=oz_cbsa; by date state cbsa_title; run; proc means data=oz_cbsa noprint; 

by date state cbsa_title; output out=oz_cbsa_stats max(oz8hrpk)=oz8hrmax n(oz8hrpk)=num_sites; 

run; data tx_oz_cbsa_stats; 

set oz_cbsa_stats; if state = "TX"; drop state _TYPE_ _FREQ_; if substr(cbsa_title,1,2) in ("Au" "Be" "Da" "El" "Ho" "Sa" “Co” “Lo”); 

run; proc sort data=tx_oz_cbsa_stats; by date; run; data days; 

format date mmddyy8.; format cbsa_title $50.; 

 date = mdy(1,1,1990); 

do while (date <= mdy(12,31,2007)); 

 cbsa_title = "Austin-Round Rock, TX"; output;  cbsa_title = "Beaumont-Port Arthur, TX"; output;  cbsa_title = "Dallas-Fort Worth-Arlington, TX"; output;  cbsa_title = "El Paso, TX"; output;  cbsa_title = "Houston-Sugar Land-Baytown, TX"; output;  cbsa_title = "San Antonio, TX"; output;  cbsa_title = "Corpus Christi, TX"; output;  cbsa_title = "Longview, TX"; output;  date = date + 1; 

end; 

run; proc sort data=days; by date cbsa_title; run; 

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data tx_oz_cbsa_days; 

merge days tx_oz_cbsa_stats; by date cbsa_title; if num_sites = . then num_sites = 0; 

run; * moving averages ;proc sort data=tx_oz_cbsa_days; by cbsa_title date; run; data tx_oz_cbsa_days2; 

set tx_oz_cbsa_days; format last_cbsa $50.; 

 last_cbsa = lag29(cbsa_title);  oz8_lag_29 = lag29(oz8hrmax);  oz8_lag_28 = lag28(oz8hrmax);  oz8_lag_27 = lag27(oz8hrmax);  oz8_lag_26 = lag26(oz8hrmax);  oz8_lag_25 = lag25(oz8hrmax);  oz8_lag_24 = lag24(oz8hrmax);  oz8_lag_23 = lag23(oz8hrmax);  oz8_lag_22 = lag22(oz8hrmax);  oz8_lag_21 = lag21(oz8hrmax);  oz8_lag_20 = lag20(oz8hrmax);  oz8_lag_19 = lag19(oz8hrmax);  oz8_lag_18 = lag18(oz8hrmax);  oz8_lag_17 = lag17(oz8hrmax);  oz8_lag_16 = lag16(oz8hrmax);  oz8_lag_15 = lag15(oz8hrmax);  oz8_lag_14 = lag14(oz8hrmax);  oz8_lag_13 = lag13(oz8hrmax);  oz8_lag_12 = lag12(oz8hrmax);  oz8_lag_11 = lag11(oz8hrmax);  oz8_lag_10 = lag10(oz8hrmax);  oz8_lag_9 = lag9(oz8hrmax);  oz8_lag_8 = lag8(oz8hrmax);  oz8_lag_7 = lag7(oz8hrmax);  oz8_lag_6 = lag6(oz8hrmax);  oz8_lag_5 = lag5(oz8hrmax);  oz8_lag_4 = lag4(oz8hrmax);  oz8_lag_3 = lag3(oz8hrmax);  oz8_lag_2 = lag2(oz8hrmax); 

18
19

 oz8_lag_1 = lag(oz8hrmax);  oz8_lag_0 = oz8hrmax; 

if ( last_cbsa = cbsa_title ) then do; 

 mov_avg30 = mean( of oz8_lag_0-oz8_lag_29 );  num_days = n( of oz8_lag_0-oz8_lag_29 ); 

if num_days <= 20 then mov_avg30 = .; 

end; else do; 

 mov_avg30 = .; num_days = .; 

end; run; 

3. Open SAS Interactive Analysis, select the tx_oz_cbsa_days2 dataset and fit variables mov_avg30 vs date b. 
6.2 Meteorologically-adjusted Trends for Ozone 

6.2.1 Houston-Galveston-Brazoria Conceptual Model Input 

In comparing the surface and upper air meteorological Omnibus Meteorological Database 

(METDAT) maintained by U.S. EPA with surface ozone measurements, some clear relationships 

can be observed. As is common in many air pollution research applications, the ozone variable 

of concern – the daily area 8-hour maximum concentration – has been transformed to create a 

“better behaved” variable in the sense that the distribution of the variable is closer to a normal 

distribution. Thus, the natural logarithm of the daily area maximum is used throughout much of 

the ensuing analysis. In addition, care has been maintained in using a consistent set of ozone 

monitors in the analysis, as prior research has shown that the number of monitors affects the 

maximum measured concentration daily. The ozone monitors used are listed in Table 6.1. All of 

these sites have nearly complete data for the 1990 – 2007 period. 

Table 6.1 Houston Area Ozone Monitoring Sites with Long-run Data 

AQS Ident Site name Latitude Longitude 

482011039 Deer Park C35 29.670 -95.128 

482011035 Clinton C403 29.734 -95.258 

482011034 East C1 29.768 -95.221 

482010075 Texas Avenue C411 29.753 -95.350 

482010062 Monroe C406 29.626 -95.267 

482010051 Croquet C409 29.624 -95.474 

482010047 Lang C408 29.834 -95.489 

482010046 North Wayside C405 29.828 -95.284 

482010029 Northwest Harris Co. C26 30.039 -95.674 

482010024 Aldine C8 29.901 -95.326 

480391016 Lake Jackson C1016 (and Clute C11) 29.044 -95.473 

The data at the deactivated Clute site in Brazoria County were connected with the time series of 

data at the currently active Lake Jackson site because of the close proximity between sites and 

the fact that an examination of the concatenated time series showed a good match. 

 

A histogram showing the counts for the number of times that each site produced the daily area 8-

hour ozone maximum over the 1995 – 2007 period is shown in Figure 6.9. 

20
Figure 6.9 Count of days for which each site presented the area maximum daily 8-hour maximum, May-Oct. 

1995-2007 

Generally the sites with the lowest frequency in Figure 6.9 are in the areas with highest NO 

emissions, and the sites with the highest frequency tend to be in downwind suburban or rural 

areas. 

As was mentioned earlier, the transformed ozone variable employed in subsequent analysis is the 

natural logarithm of the area 8-hour maximum, which is referred to as “l_oz8”. Only data from 

May through October (seven months) for the years 1995 – 2007 (13 years) were used to match 

the available METDAT data. 

The variables in the METDAT database are described in Table 6.2. The ozone data from each 

urban area were merged with the corresponding day’s METDAT data from the nearest upper air 

monitoring location. In most cases the associated upper air site was located within or adjacent to 

is nearest urban area. The exception is Austin, which is just under 50 miles from the upper air 

site at Fort Hood. This may in part explain why Austin correlations are weaker than average 

among the eight areas considered. The Pearson correlations of l_oz8 with the METDAT 

variables for each large metropolitan area in Texas are shown in Table 6.3. The correlations for 

Houston and Dallas/Fort Worth are among the best. The correlation values are color coded to 

highlight the best relationships. The color-coding is blue for less than -0.3 and orange for greater 

than 0.3. The black-bolded rows correspond to the variables used in the Camalier, Cox, and 

Dolwick analysis5

. Because El Paso is located above the typical 925 mb altitude, some 

METDAT parameters are missing. The columns in Table 6.3 are grouped based on a separate 

factor analysis of the behavior of regional ozone concentrations in Texas and on the common 

 

5 Camalier, L., Cox, W., Dolwick, P., 2007. The effects of meteorology on ozone in urban areas and their use in assessing ozone 

trends. Atmospheric Environment 41, 7127-7137.

21
22

behavior of the correlations within this table. Thus, South Texas areas are grouped, Central and 

Upper Gulf Coast areas are grouped, and Dallas/Fort Worth and El Paso stand alone. It should 

be noted that many lower correlations not highlighted may be statistically significant because of 

the large number of available observations. 

Examples of the behavior of the relationships for three of the highest absolute value correlations 

for the Houston area are shown in Figures 6.10, 6.11, and 6.12. Figure 6.10 shows that the ozone 

maximum is negatively related to the distance the large scale air mass has moved during the 

preceding 24 hours, supporting the well-known relationship between stagnation and pollutant 

accumulation. Figure 6.11 shows the important fact that the drier an air mass, the higher the 

ozone concentration. Caution must be used in assessing this relationship, as “Gulf” air is 

generally cleaner and more humid than “Continental” air, so “humidity” may be related to 

“transport” variables. However, the same relative humidity variable is negative and significant 

in Dallas and El Paso, the two regions farthest from the Gulf. Figure 6.12 shows that the 

difference between the maximum and minimum temperature is strongly positively related to the 

maximum area ozone, and this variable is a better predictor for ozone than maximum 

temperature alone (see Table 6.3 on a following page). One hypothesis may be that a bigger 

swing in temperatures over the course of a day may increase evaporation of hydrocarbons. 

For the Houston-Galveston-Brazoria region, an ordinary least squares regression analysis of 

l_oz8 on up to 51 of the variables in the METDAT list explains 73 percent in the variance for 

daily area maximum ozone. Using a more parsimonious model with only the best set of 10 

largely independent variables reduces the R2

 to 47 percent. Implementing a more complicated 

parsimonious general linear model (GLM) with spline functions to reduce short-term random 

variations and to accommodate nonlinear relationships and including a circular spline function

for the TRANDIR (net direction of HYSPLIT back-trajectory) variable plus including a set of 

dummy variables for “year”, the R2 improves to 67 percent. This GLM regression approach 

shows no day-of-the week effect, but does show a statistically significant decline in 

meteorologically-adjusted ozone levels from 1995 – 2007. More details on the GLM regression 

approach are provided in sections 6.2.4 and 6.2.5 of this report. 
23

Table 6.2 Variables from the METDAT Database 

ID NAME UNIT DURATION PARM 

1 Maximum surface temperature C 24-hr calc TMAX 

2 Morning (7-10 a.m. LST) average temperature C 4-hr calc TAVGAM 

3 Afternoon (1-4 p.m. LST) average temperature C 4-hr calc TAVGPM 

4 Diurnal temperature change C 24-hr calc TDELTA 

5 Average daily wind U vector (m/s) m/s 24-hr calc UAVG 

6 Average daily wind V vector (m/s) m/s 24-hr calc VAVG 

7 Average daily wind speed (m/s) m/s 24-hr calc WSAVG 

8 Average daily wind direction (deg) deg 24-hr calc WDAVG 

9 Average morning (7-10 a.m. LST) wind U vector (m/s) m/s 4-hr calc UAVGAM 

10 Average morning (7-10 a.m. LST) wind V vector (m/s) m/s 4-hr calc VAVGAM 

11 Average morning (7-10 a.m. LST) wind speed (m/s) m/s 4-hr calc WSAVGAM 

12 Average morning (7-10 a.m. LST) wind direction (deg) deg 4-hr calc WDAVGAM 

13 Average afternoon (1-4 p.m. LST) wind U vector (m/s) m/s 4-hr calc UAVGPM 

14 Average afternoon (1-4 p.m. LST) wind V vector (m/s) m/s 4-hr calc VAVGPM 

15 Average afternoon (1-4 p.m. LST) wind speed (m/s ) m/s 4-hr calc WSAVGPM 

16 Average afternoon (1-4 p.m. LST) wind direction (deg) deg 4-hr calc WDAVGPM 

17 Average daily relative humidity (%) % 24-hr calc RHAVG 

18 Midday (10 a.m.-4 p.m. LST) average relative humidity (%) % 7-hr calc RHAVGMID 

19 Nighttime (8 p.m.-7 a.m. LST) average relative humidity (%) % 12-hr calc RHAVGNIGHT 

20 Average dew point temperature C 24-hr calc DPAVG 

21 Maximum dew point temperature C 24-hr calc DPMAX 

22 Maximum water vapor mixing ratio (g/kg) g/kg 24-hr calc MRMAX 

23 Average Sea level pressure (mb) mb 24-hr calc SLPAVG 

24 Average Station pressure (mb) mb 24-hr calc STPAVG 

25 Minimum apparent temperature C 24-hr calc TAMIN 

26 Average apparent temperature C 24-hr calc TAAVG 

27 Maximum apparent temperature C 24-hr calc TAMAX 

28 Morning (7-10 a.m. LST) average fractional cloud cover (tenths) tenths 4-hr calc CCAVTAM 

29 Afternoon (1-4 p.m. LST) average fractional cloud cover (tenths) tenths 4-hr calc CCAVGPM 

30 Total precipitation (inches) in 24-hr calc PRECIP 

31 Occurrence of rain (hours) hrs 24-hr calc RAINHRS 

32 Occurrence of haze (hours) hrs 24-hr calc HAZEHRS 

33 Occurrence of fog (hours) hrs 24-hr calc FOGHRS 

34 Morning (~1200 UTC) Height of 500 mb surface (m) m 1-hr obs H500 

35 Morning (~1200 UTC) Height of 700 mb surface (m) m 1-hr obs H700 

36 Morning (~1200 UTC) Height of 850 mb surface (m) m 1-hr obs H850 

37 Morning (~1200 UTC) Temperature at 925 mb C 1-hr obs T925 

38 Morning (~1200 UTC) Temperature at 850 mb C 1-hr obs T850 

39 Morning (~1200 UTC) Temperature at 700 mb C 1-hr obs T700 

40 Morning (~1200 UTC) Temperature at 500 mb C 1-hr obs T500 

41 Morning (~1200 UTC) Dewpoint Temperature at 850 mb C 1-hr obs TD850 

42 Morning (~1200 UTC) 925 mb-surface temperature difference C 1-hr calc DT925 

43 Morning (~1200 UTC) 850 mb-surface temperature difference C 1-hr calc DT850 

44 Morning (~1200 UTC) 700 mb-surface temperature difference C 1-hr calc DT700 

45 Maximum rate of mixing height increase (m/hr) 6am-12pm LST m/hr 7-hr calc MRMHI 

46 Maximum mixing height (m) 4am-4pm LST m 13-hr calc MHMAX 

47 24-hr Transport Direction deg 24-hr calc TRANDIR 

48 24-hr Transport Distance km 24-hr calc TRANDIS 

49 X-component of the 24-hr Transport Vector km 24-hr calc TRANU 

50 Y-component of the 24-hr Transport Vector km 24-hr calc TRANV 

51 Z-component of the 24-hr Transport Vector km 24-hr calc TRANW 

52 24-hr Scalar Wind Run km 24-hr calc WNDRUN 

53 24-hr Change in Morning (~1200 UTC) Temperature at 850 mb C 1-hr calc D24T850 

54 24-hr Change in Morning (~1200 UTC) Dewpoint Temperature at 850 mb C 1-hr calc D24TD850 

55 Deviation in Morning (~1200 UTC) Temperature at 850 mb from 10-yr monthly mean C 1-hr calc DEVT850 

56 Deviation in Morning (~1200 UTC) Temperature at 700 mb from 10-yr monthly mean C 1-hr calc DEVT700 

57 Deviation in Morning (~1200 UTC) Temperature at 500 mb from 10-yr monthly mean C 1-hr calc DEVT500 

58 Deviation in Morning (~1200 UTC) Height of 850 mb surface (m) from 10-yr monthly mean m 1-hr calc DEVH850 

59 Deviation in Morning (~1200 UTC) Height of 700 mb surface (m) from 10-yr monthly mean m 1-hr calc DEVH700 

60 Deviation in Morning (~1200 UTC) Height of 500 mb surface (m) from 10-yr monthly mean m 1-hr calc DEVH500 
24

Table 6.3 Correlations of l_oz8 with METDAT variables, May-Oct. 1995 - 2007 

ln(area oz8hrpk) Austin BPA HGB San Antonio DFW Victoria CC El Paso 

Num obs 2,336 2,379 2,387 2,381 2,392 2,222 2,343 2,391 

NOx 0.21 0.24 -0.17 

Isoprene 0.31 0.42 0.00 

HRVOC 0.08 0.19 -0.08 

Alkanes 0.03 0.18 -0.04 

BTEX 0.17 0.24 -0.04 

CCAVGPM -0.37 -0.35 -0.42 -0.36 -0.42 -0.17 -0.07 -0.13 

CCAVTAM -0.46 -0.51 -0.50 -0.51 -0.53 -0.36 -0.21 -0.21 

D24T850 0.04 -0.04 -0.04 0.00 0.08 -0.05 -0.08 0.09 

D24TD850 -0.07 -0.03 -0.04 -0.05 0.00 -0.05 -0.04 -0.02 

DEVH500 0.04 -0.13 -0.09 -0.04 0.17 -0.10 -0.15 0.18 

DEVH700 0.11 -0.06 -0.06 0.04 0.21 0.03 -0.03 0.20 

DEVH850 0.17 0.05 0.02 0.10 0.20 0.16 0.11 0.11 

DEVT500 -0.06 -0.09 -0.04 -0.06 0.04 -0.11 -0.11 0.00 

DEVT700 -0.09 -0.15 -0.08 -0.11 0.02 -0.24 -0.24 0.16 

DEVT850 -0.02 -0.20 -0.14 -0.06 0.10 -0.20 -0.23 0.14 

DPAVG -0.36 -0.28 -0.23 -0.31 0.02 -0.48 -0.53 0.04 

DPMAX -0.33 -0.24 -0.20 -0.25 0.03 -0.45 -0.50 0.06 

FOGHRS -0.12 0.07 -0.04 -0.11 -0.22 0.14 0.19 -0.15 

HAZEHRS -0.18 0.18 0.01 -0.03 -0.09 0.18 0.20 -0.06 

MRMAX -0.37 -0.26 -0.23 -0.28 -0.01 -0.49 -0.54 0.06 

RAINHRS -0.39 -0.32 -0.41 -0.40 -0.41 -0.25 -0.10 -0.16 

RHAVG -0.56 -0.45 -0.46 -0.55 -0.56 -0.42 -0.33 -0.27 

RHAVGMID -0.53 -0.56 -0.54 -0.55 -0.59 -0.45 -0.28 -0.33 

RHAVGNIGHT -0.48 -0.17 -0.23 -0.40 -0.45 -0.26 -0.25 -0.23 

SLPAVG 0.06 0.09 0.09 0.03 -0.01 0.10 0.12 -0.13 

STPAVG 0.08 0.09 0.15 0.05 -0.07 0.10 0.12 0.06 

TAAVG 0.00 -0.12 -0.03 0.01 0.36 -0.34 -0.47 0.44 

TAMAX 0.11 0.04 0.09 0.13 0.44 -0.18 -0.37 0.49 

TAMIN -0.15 -0.25 -0.15 -0.17 0.23 -0.46 -0.52 0.31 

TAVGAM 0.15 0.02 0.04 0.14 0.45 -0.24 -0.39 0.43 

TAVGPM 0.24 0.26 0.32 0.30 0.56 0.03 -0.20 0.52 

TDELTA 0.49 0.59 0.48 0.46 0.47 0.52 0.30 0.29 

TMAX 0.22 0.23 0.23 0.25 0.52 -0.02 -0.24 0.51 

TRANDIS -0.47 -0.40 -0.45 -0.41 -0.49 -0.32 -0.21 -0.20 

TRANU 0.20 0.19 0.19 0.20 0.07 0.14 0.09 -0.12 

TRANV -0.39 -0.48 -0.44 -0.32 -0.17 -0.53 -0.58 -0.08 

TRANW 0.25 0.23 0.20 0.12 0.09 0.24 0.21 0.17 

UAVG 0.07 0.03 0.07 0.15 -0.12 0.04 0.08 -0.13 

UAVGAM 0.11 -0.08 0.00 0.09 -0.01 -0.12 -0.05 -0.11 

UAVGPM -0.10 -0.05 -0.09 -0.03 -0.18 -0.06 0.02 -0.10 

VAVG -0.28 -0.37 -0.27 -0.19 -0.07 -0.45 -0.50 -0.01 

VAVGAM -0.27 -0.44 -0.39 -0.24 -0.09 -0.50 -0.56 0.02 

VAVGPM -0.29 -0.28 -0.20 -0.13 -0.05 -0.43 -0.46 -0.05 

WNDRUN -0.44 -0.39 -0.44 -0.38 -0.47 -0.28 -0.17 -0.20 

WSAVG -0.37 -0.35 -0.34 -0.36 -0.39 -0.31 -0.24 -0.24 

WSAVGAM -0.33 -0.29 -0.32 -0.36 -0.42 -0.24 -0.20 -0.27 

WSAVGPM -0.30 -0.35 -0.25 -0.26 -0.31 -0.35 -0.31 -0.30 

DT925 0.19 0.36 0.21 0.16 0.30 0.29 
Figure 6.10 Scatterplot l_oz8 vs TRANDIS (distance from HGB to last endpoint in 24-hr HYSPLIT backtraj), r=-0.45 

Figure 6.11 Scatterplot l_oz8 vs RHAVMID (mid-day relative humidity, %), r=-0.54 

25
Figure 6.12 Scatterplot l_oz8 vs TDelta (daily maximum minus minimum temperature °C), r=0.48 

26
6.2.2. Assessing Transport Affecting the Houston Region Using HYSPLIT 

Back-trajectories Alone

The National Oceanic and Atmospheric Administration (NOAA) Air Resource Laboratory 

(ARL) maintains software, data files, and documentation useful in assessing pollution transport. 

The HYSPLIT 4.86 desktop computer application was used with Eta Data Assimilation System 

(EDAS) datasets to assess the year to year variability of regional and continental scale winds, 

and the relationship of the winds to surface level ozone concentrations in Houston. 

Back-trajectories were run for all days with available data from May 1 and October 31, 2000 – 

2007. The common starting point for all runs was in central Houston, TX (29.76° north latitude, 

95.36° west longitude), 300 meters above ground level. Trajectories were started at 18 

Coordinated Universal Time (UTC), which equals noon, Central Standard Time (CST), and were 

run backwards in time for 72 hours (three days). The location of the starting point appears in 

Figure 6.13 on a local scale and Figure 6.14 on a continental scale. 

An individual back-trajectory is an idealized hypothetical center-line for the broad path that 

incoming air likely took in moving into an area. Upwind pollution sources could be near or 

faraway along that path, and relatively far from that modeled centerline. However, when 

multiple monitors spread over a larger area are used – such as a county, a metropolitan area 

(MSA), or an air quality control region (AQCR) – and assuming these monitors are historically 

well correlated and all are measuring similar concentration levels on a given day, then the 

associated back-trajectory is more meaningful in suggesting that the pollution has been 

transported into the area from a location relatively near the modeled centerline. Furthermore, 

when many trajectories are used and a correlation is established between concentrations and 

particular trajectory directions, shapes, and lengths, then one can conclude that some upwind 

source areas and/or pathways have a causative relation with higher than average concentrations, 

and some other areas/paths are associated with cleaner air and lower concentrations. 

 

6

 Draxler, R.R. and Rolph, G.D., 2003. HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Model 

access via NOAA ARL READY Website (http://www.arl.noaa.gov/ready/hysplit4.html). NOAA Air Resources 

Laboratory, Silver Spring, MD. 

Rolph, G.D., 2003. Real-time Environmental Applications and Display sYstem (READY) Website 

(http://www.arl.noaa.gov/ready/hysplit4.html). NOAA Air Resources Laboratory, Silver Spring, MD. 

. 

27
Figure 6.13 Starting point for HYPLIT 72-hour back-trajectories from 300 m AGL, 18 UTC, meso-scale 

28
Figure 6.14 Starting point for HYPLIT 72-hour back-trajectories from 300 m AGL, 18 UTC, synoptic scale 

In this particular application, three major steps were taken. 

• First, as was mentioned above, daily synoptic-scale back-trajectories were created. 

• Second, a clustering algorithm built into HYPSLIT was used to sequester individual 

back-trajectories into a relatively small set of classes based on shape and direction. 

(Details on the clustering algorithm originally composed for an earlier contract report 

appears below.) The mean centerlines for the six identified clusters appears in Figure 

6.15. The clusters of trajectories appear in Figures 6.17 – 6.22. Figure 6.16 shows a set 

of trajectories which were not contained in any cluster, and have been assigned cluster 0. 

All but one of these cluster 0 trajectories run to the south and would go beyond the 

modeling domain for the EDAS data, so they do represent a meaningful cluster to 

themselves with one outlier. Figure 6.23 shows a histogram summarizing the number of 

trajectories per cluster. 

• In a separate process, the ozone data were analyzed to select a set of monitors with a 

nearly continuous record of operation over the 2000 – 2007 period. Using 12 such sites 

selected to give good regional coverage, the daily area-wide maximum and daily areawide minimum 8-hour ozone maximum by site (oz8hrpk) were collected. The max was 

29
identified as the “oz_area_max” and the min was identified as the “background”. The 12 

ozone sites are depicted in Figure 6.24. 

• Next, the file with the variable identifying the cluster classification for each day was 

merged with a file containing the ozone data for each day. 

• Lastly, the statistics for mean “oz_area_max” and “background” and the daily difference 

between them – “difference” – were calculated as functions of the trajectory 

classification. 

The results show conclusively that trajectory cluster has a highly significant affect on local 

“oz_area_max” and “background” values. These results are summarized later in this section. 

Figure 6.15 Means of seven 60-hour back-trajectory clusters, May-October 2000 – 2007, Houston origin 300 

m AGL, 18 UTC: 0. (not shown) far southerly fetch; 1. red, SE fetch; 2. blue, SSE fetch; 3. green, northerly 

fetch; 4. cyan, short fetch; 5. magenta, NE fetch; 6. yellow, easterly fetch 

30
Figure 6.16 Cluster 0 72-hour back-trajectories from Houston 

Figure 6.17 Cluster 1 72-hour back-trajectories from Houston 

31
Figure 6.18 Cluster 2 72-hour back-trajectories from Houston 

Figure 6.19 Cluster 3 72-hour back-trajectories from Houston 

32
Figure 6.20 Cluster 4 72-hour back-trajectories from Houston 

Figure 6.21 Cluster 5 72-hour back-trajectories from Houston 

33
Figure 6.22 Cluster 6 72-hour back-trajectories from Houston 

Figure 6.23 Histogram on number of trajectories in each cluster 

40

217

512

49

314

167 157

0 1 2 3 4 5 6

Cl us t er

0

200

400

F

r

e

q

u

e

n

c

y

34
Figure 6.24 Sites used in ozone area max and background determination 

How Trajectory Clustering Works 

(This text first appeared in a report under another contract in 2007.) Clustering works by 

successively combining smaller clusters into larger ones based on some criteria. Initially, all 

elements (trajectories, in this case) are their own clusters7

. An individual trajectory has zero 

“spatial variance.” A distance measure between pairs of trajectories is calculated, and the pair 

closest together according to the distance measure is joined as a cluster, represented by a cluster 

mean. The process is repeated merging individual trajectories or clusters based on minimizing 

each step’s effect on increasing the total spatial variance (TSV) within all clusters. In the early 

steps of clustering, the TSV may increase as each new small cluster has variability within it that 

individual trajectories do not have. At some point the number of clusters will have grown and 

the change in TSV from step to step becomes small and steady. However, eventually there is a 

 

7

 Stunder, B.J., 1996, “An Assessment of the Quality of Forecast Trajectories,” J. Appl. Meteor., 35, 1319–1331. 

35
relatively large jump in TSV, as two dissimilar clusters are joined. Thus, the number of clusters 

just before the jump is a good choice for total number of factors. In Figure 6.25, a jump occurs 

from six to five clusters, with another sharp jump from four to three clusters. An examination of 

the six-cluster results suggests this choice is best at reflecting the diversity of air flows that vary 

in both direction and length of fetch. 

Figure 6.25 Change in total spatial variance by step in cluster process 

Final Results from Relating HYSPLIT Back-trajectories to Ozone in Houston 

Table 6.4 is the statistical summary for the “oz_area_max” and “background” ozone for each day 

by cluster. The table shows that clusters 4 for “short fetch” and 5 for “northeast fetch” have the 

highest values for both oz_area_max and background. The average difference between the daily 

oz_area_max and background data is shown in the “difference mean” column, and the ratio 

between the difference and the average of oz_area_max and background is shown in the 

“difference %” column. Although clusters 4 and 5 also have the highest “difference mean”, they 

are in the mid-to-low range in terms of “difference %”. 

Table 6.4 Statistics for mean area max / min oz8hrpk by HYSPLIT back-trajectory cluster, 2000-2007 data 

Cluster Fetch Count 

oz_area_max 

mean 

background 

mean 

difference 

mean 

difference 

% 

0 

far 

SE 40 45.0 23.8 21.1 61% 

1 ESE 217 40.6 21.4 19.1 62% 

2 SE 506 48.5 24.8 23.7 65% 

3 North 49 59.1 37.2 21.9 45% 

4 Short 312 71.6 40.8 30.8 55% 

5 NE 166 73.3 45.1 28.1 48% 

6 East 151 61.1 35.6 25.5 53% 

36
Table 6.5 shows the results of calculating approximate 95 percent confidence intervals on the 

mean of area max by cluster. A simple approach was used in selecting two standard errors for 

the confidence intervals in each row. The table shows the differences between means is 

statistically significant in most pair-wise comparisons. 

Table 6.5 Mean area max approx. 95% confidence intervals, generally not overlapping beyond one or two 

rows 

Cluster ranked 

by max mean Fetch Count 

oz8hr_max 

mean 

mean max + 

2se 

mean max - 

2se 

1 ESE 217 40.6 42.7 38.5 

0 far SE 40 45.0 51.5 38.4 

2 SE 506 48.5 50.3 46.6 

3 North 49 59.1 65.2 53.0 

6 East 151 61.1 64.5 57.8 

4 Short 312 71.6 74.3 68.9 

5 NE 166 73.3 76.7 69.8 

Care was taken in selecting the 12 ozone monitoring sites used in the assessment to maximize the 

inclusion of clear upwind and downwind monitoring sites. The Harris NW CAMS 26, the Lake 

Jackson / Clute combined site pair, and the Galveston Airport / 99th St combined site pair 

generally represent the background sites, with sites on the downwind side of the urban core and 

ship channel generally representing the area max sites. However, the true “background” ozone 

may be farther away in a more rural area on many days, and true area max may be 

underestimated. Nevertheless, the large number of observations used in this assessment makes 

clear that both the observed area max and observed area min are affected by the backtrajectories. Figure 6.26 shows that the relationship between area min and max by cluster is 

linear and significant (p<0.01). This suggests that the trajectory cluster is a factor in severity for 

both the area max and the area background. 

Figure 6.26 also shows that the difference between area max and min is also roughly linear with 

area max, but with a lower slope and overall statistical significance (p=0.03). This second graph 

on difference may suggest the local contribution may also be related to back trajectories. An 

explanation for this is that the slower air movement of the “short” cluster 4 allows greater 

accumulation in the area under lower local surface wind speeds. Another explanation for “east” 

cluster 6 and “NE” cluster 5 is that winds from these directions help to move surface air along 

the ship channel region and across the central city, an alignment of winds and sources shown in 

the TexAQS II Rapid Science Synthesis (RSS) report to lead to very high ozone readings. 

The bottom line is that variation in synoptic scale back trajectories can account for around 24 

ppb of variation in background concentrations and around 33 ppb in area max on average 

(comparing Cluster 1 and Cluster 5 results). 

37
Figure 6.26 Relationship between mean max and mean min (background) by cluster, and relationship 

between “local contribution” (difference max – min) by cluster 

y = 1.36x + 12.76

R2

 = 0.96

y = 0.36x + 12.67

R2

 = 0.65

15

25

35

45

55

65

75

85

15 20 25 30 35 40 45 50

Area wide minimum (background) ppb

Area wide max (blue), and Delta (max - min) pink

oz_area_max mean

difference mean

Linear (oz_area_max mean)

Linear (difference mean)

4 short

5 NE

6 east

3 north

2 SE

1 ESE

0 far SE

38
6.2.3 Factor Analysis to Assess Air-shed Delineations 

In an early report on this project, a factor analysis using historical ozone data (1995-2007) was 

used to delineate geographic areas with distinct ozone concentration behavior. Later this 

analysis re-done using data from 2005-2007. As was the case in the earlier model, only data 

from May through October were used. The total number of days available over the three year 

period is 552. The number of days that could be used based on the data completeness thresholds 

tests was 306. The long-term (1995-2007) data provided for 43 counties to contribute to the 

factor analysis. In the more recent data model, 71 counties provide data. These include 

relatively new monitoring in counties surrounding the Dallas-Fort Worth urban core, monitors 

sited for the TexAQS II study, relatively new monitors in other Region 6 states, and the Big 

Bend site in West Texas. Some TexAQS II sites were excluded for low data counts. 

The 2005-2007 analysis suggests seven factors 

• West Texas/ Southern New Mexico 

• Northern New Mexico 

• Oklahoma 

• Arkansas 

• Southern Louisiana and southeast Texas 

• South Texas 

• East Texas. 

The best results were reached with an oblique “quartimin” rotation. A table of the rotated factor 

loadings with geographic labels appears in Table 6.6. The table is organized to group counties 

with similar loadings on a given factor. Cells are color-coded by loading – yellow for 0.30-0.50, 

orange for 0.50-0.70, and red for greater than 0.70. As should be expected, significant overlap 

exists, and some counties (e.g., Montgomery-48339 and Harris-48201) clearly settle in two or 

three. A map of EPA Region 6 illustrating the counties listed above with the clusters of factors 

found appears in Figure 6.27. Ovals are drawn to represent the large uncertainty in actual air 

shed boundaries and the considerable overlap across factors for many counties as seen in the 

factor loadings in Table 6.6. 

39
Table 6.6 Rotated factor loadings for EPA R6 counties 

40
Figure 6.27 EPA R6 regional ozone factors 2005-2007 data 

41
6.2.4 GLM Development 

The first step in the development of the model following the EPA Camalier et al. method is to 

examine the relationship between the chosen ozone variable and the meteorological data. The 

correlations were discussed in section 6.2.1. Recalling the graphs in Figures 6.8 – 6.10 that 

showed “l_oz8” versus “trandis”, rhavgmid”, and “tdelta”. In each of these, a linear fit was 

applied. A “linear” fit is one instance of a model that is a smooth and connected curve. Many 

data analysts are familiar with other examples of fitting models; e.g., quadratic fits. A more 

general method of modeling is to use splines. Splines in general are collections of functions, 

usually polynomials, fit to segments of the data and connected such that the y-value and first and 

second derivatives of the conjoining functions are equal. One example of this approach appears 

in Figure 6.28. In selecting a spline model, a user must consider trade-offs between smoothness 

and the model fit. The number and location of “knots” that are the endpoints of the connected 

model segments are user controlled. SAS spline calculations are predicated on balancing the 

improved fit against the increased roughness in the curve using a penalty function incorporated 

into the ordinary least squares objective function. 

Figure 6.28 Generic example of a spline model fit to data 

The approach we follow in this project is a general linear model in the sense that we assume a 

log-link function for ozone and do not assume normality for the independent variables. The 

implementation in SAS is via PROC TRANSREG (transformation regression). This procedure 

fits linear models, optionally with spline and other nonlinear transformations, in “simple, 

multiple, and multivariate regression with variable transformations.”8

 More details on the 

approach we use will be provided in the training class, and a more detailed written element can 

be added upon request. 

 

8

 SAS 9.1 PROC TRANSREG documentation. 

42
6.2.5 Results 

The GLM model used by Camalier, Cox, and Dolwick and found on the EPA Trend Website 

coded in the R programming language was approximately recoded in SAS. Only an approximate 

match was achieved with the Cincinnati, OH test data on the EPA Trends Website, but it was 

judged to be acceptable. A comparison based on using data posted on the EPA Tend Web site 

for Cincinnati, OH appears in Figure 6.29. 

Figure 6.29 PROC TRANSREG results for EPA Trend Website Cincinnati, OH data compared with R-code 

results 

y = 1.02x - 0.65

R2

 = 0.99

58

59

60

61

62

63

64

65

56 58 60 62 64 66

CCD R-code Cincinnati met-explained O3 ppb

Proc Transreg Cincinnati 

met-explained O3 ppb

The exact nature of the GLM procedure in R and the algorithmic match in SAS was investigated 

by examining documentation on PROCs TRANSREG, GLM, GAM (experimental), and 

GLIMIX (experimental). Accuracy of results and ease of use argued for TRANSREG. 

The graphs showing the annual time series for annual mean 8-hour daily max and annual mean 

ozone as a function of meteorology appear in even-numbered Figures 6.30 – 6.44. The oddnumbered Figures 6.31 – 6.45 show the result of subtracting the meteorologically-produced 

ozone from the actual ozone. This, we posit, is the sum of anthropogenic factors, model error, 

and undiagnosed meteorological factors. If we assume the model error and undiagnosed 

meteorological factors are relatively constant over time, then the trends in the odd-numbered 

figures represent the effect of chemical emissions reductions. 

The statistical results are summarized in Table 6.7 following the graphs. The bottom line is 

that after adjusting for meteorology, concentrations of ozone have declined significantly in 

the Dallas-Fort Worth and Houston areas. All else held equal, one would expect from 

around 6 ppb lower 8-hour daily maxima in DFW in 2007 vs. 1996, and around 9 ppb 

lower 8-hour daily maxima in HGB in 2007 vs. 1996. Other areas do not show significant 

trends over the same period.

43
Figure 6.30 Austin-Round Rock actual annual summer-mean and meteorologically-adjusted time series 

Figure 6.31 Austin-Round Rock residual meteorologically-adjusted trend model 

44
Figure 6.32 Beaumont-Port Arthur actual annual summer-mean and meteorologically-adjusted time series 

Figure 6.33 Beaumont-Port Arthur residual meteorologically-adjusted trend model 

45
Figure 6.34 Corpus Christi actual annual summer-mean and meteorologically-adjusted time series 

Figure 6.35 Corpus Christi residual meteorologically-adjusted trend model 

46
Figure 6.36 Dallas-Fort Worth actual annual summer-mean and meteorologically-adjusted time series 

Figure 6.37 Dallas-Fort Worth residual meteorologically-adjusted trend model 

47
Figure 6.38 El Paso actual annual summer-mean and meteorologically-adjusted time series 

Figure 6.39 El Paso residual meteorologically-adjusted trend model 

48
Figure 6.40 Houston-area actual annual summer-mean and meteorologically-adjusted time series 

Figure 6.41 Houston-area residual meteorologically-adjusted trend model 

49
Figure 6.42 San Antonio actual annual summer-mean and meteorologically-adjusted time series 

Figure 6.43 San Antonio residual meteorologically-adjusted trend model 

50
Figure 6.44 Victoria actual annual summer-mean and meteorologically-adjusted time series 

Figure 6.45 Victoria residual meteorologically-adjusted trend model 

51
Table 6.7 Summary statistics for met-adjusted ozone trends1996-2007, red values statistically significant 

Location Intercept Slope t P-value Lag 1 Coef. t 

Austin 0.369 0.16 1.43 0.19 0.30 0.95 

Beaumont 1.445 -0.04 -0.18 0.86 0.22 0.68 

Corpus Christi 3.239 -0.40 -1.73 0.12 -0.13 -0.39 

Dallas 4.244 -0.57 -3.92 0.00 -0.20 -0.60 

El Paso 0.400 0.09 0.46 0.66 0.20 0.61 

Houston 6.438 -0.86 -4.20 0.00 -0.20 -0.63 

San Antonio 0.076 0.15 0.59 0.57 -0.32 -1.01 

Victoria 2.624 -0.31 -1.54 0.16 -0.27 -0.83 

The analyses for Dallas , Houston, and El Paso have been re-run with the addition of morning 

NOx and hydrocarbons from the PAMS monitoring stations in these cities as predictor variables 

in the model, along with the same meteorological variables as in the previous analysis. The 

following figures show the results in a format similar to that above. Note that the strength of the 

regressions shown in Table 6.8 has been weakened as more of the factors causing ozone have 

been explained. 

52
Figure 6.46 Dallas actual annual summer-mean and meteorologically and precursor predicted time series 

Figure 6.47 Dallas residual meteorologically and precursor adjusted trend model 

53
Figure 6.48 El Paso actual annual summer-mean and meteorologically and precursor predicted time series 

Figure 6.49 El Paso residual meteorologically and precursor adjusted trend model 

54
Figure 6.50 Houston actual annual summer-mean and meteorologically and precursor predicted time series 

Figure 6.51 Houston residual meteorologically and precursor adjusted trend model 

Table 6.8 Second round of regressions with NOx and hydrocarbons included, explaining for of the cause of 

ozone, leaving less of a trend over time 

location Intercept Coef. t p-value Lag1 Coef. t 

Dallas 1.357 -0.154 -2.56 0.031 0.244 0.75 

El Paso -0.027 0.075 0.77 0.463 0.121 0.37 

Houston 2.859 -0.361 -1.68 0.128 -0.183 -0.56 

55
7. Oxides of Nitrogen Trends 

7.1 Oxides of Nitrogen OLS Trends for the Houston Region 

The Ordinary least square (OLS) trends were developed after an examination of the diurnal 

patterns during the ozone season (May – October) in years since 2000. After the examination of 

individual monitors from urban areas across the state, a representative subset of nine sites (AQS 

identification numbers 482010024, 481130069, 481410037, 482450009, 484391002, 

480290046, 482011039, 484530020, 481670014) were selected to develop a typical diurnal 

pattern. This is shown in Figure 7.1. From this graph we chose to use the four-hour 5:00 – 8:59 

CST period (hours beginning at 5, 6, 7, and 8 CST) for trends. This is the maximum period for 

NO mean concentration, which is generally modeled as 90 percent of fresh NOx emissions by 

mass. NO2 is clearly bi-modal with nearly equal peaks in morning and nighttime values. The 

resulting NOx, ideally the sum of NO and NO2, is also bi-model with the clear maximum values 

at the hours beginning at 6 and 7 CST. 

Figure 7.1 Composite NOx Diurnal Pattern for Houston 

Using the data for each day from the hours and months specified, we calculated the average 

concentration by year. In order to improve the numerical accuracy of the regressions and to 

56
make the trends more easily interpretable, a “year index” variable was created by subtracting 

1990 from each year. We regressed the annual mean morning concentrations on the year index 

and produced tabular and graphical results. The calculated y-intercept is directly relatable to the 

expected value for the mean concentration in 1990, which is easily comparable to the most recent 

year through the regression equation. The calculated slope, of course, is in units of ppb per year. 

The regression statistics and parameter estimates appear in the tables, while the graphs show the 

scatterplots of the data, the OLS line fits to the data, and the regression equations for the lines at 

the bottom of the figures. Unfortunately, SAS GPLOT does not allow one to reposition or 

reformat the regression equation as does MS Excel. 

The OLS trends for NOx in the Houston Region were developed using data from nine sites listed 

in Table 7.1, all of which have at least 10 years of data. The Galveston CAMS 34 site with 10 

years of data (DoF + 2) shows a weak not-significant positive trend for NO, NO2, and NOx, but 

the concentrations are extremely low, as evidenced by the y-intercepts. The urban Bayland Park 

CAMS 53 site shows a weak not-significant negative trend for all three variables, also with 10 

years of data. Otherwise, the other seven sites, all with a longer time series of data, show 

statistically significant downward trends for total NOx, five show statistically significant 

downward trends for NO, and five show statistically significant downward trends for NO2. 

Table 7.1 Ordinary least squares trends for 5-8 CST mean NOx, NO, and NO2 in the Houston Region 

Site Description Species Intrcpt Slope DoF Rsq T p-val L95B U95B

Galveston Aprt C34 nox 1.55 0.36 8 35% 2.06 0.07 -0.04 0.75

Galveston Aprt C34 no 0.34 0.06 8 8% 0.85 0.42 -0.10 0.21

Galveston Aprt C34 no2 2.70 0.20 8 24% 1.60 0.15 -0.09 0.49

Aldine C8 nox 37.82 -0.53 16 25% -2.30 0.04 -1.01 -0.04

Aldine C8 no 22.93 -0.51 16 41% -3.30 0.00 -0.83 -0.18

Aldine C8 no2 15.11 -0.02 16 0% -0.2 0.84 -0.28 0.23

NW Harris Co. C26 nox 16.59 -0.37 9 45% -2.72 0.02 -0.68 -0.06

NW Harris Co. C26 no 7.27 -0.24 9 35% -2.2 0.05 -0.48 0.01

NW Harris Co. C26 no2 12.08 -0.31 9 79% -5.90 0.00 -0.43 -0.19

Lang C408 nox 70.01 -1.75 16 67% -5.67 0.00 -2.40 -1.09

Lang C408 no 48.54 -1.54 16 73% -6.63 0.00 -2.03 -1.04

Lang C408 no2 22.33 -0.22 16 28% -2.5 0.02 -0.41 -0.03

Bayland Park C53 nox 35.51 -0.54 8 9% -0.89 0.40 -1.93 0.86

Bayland Park C53 no 19.12 -0.47 8 16% -1.23 0.25 -1.33 0.40

Bayland Park C53 no2 17.27 -0.12 8 3% -0.49 0.64 -0.68 0.44

East C1 nox 58.56 -1.30 13 69% -5.37 0.00 -1.82 -0.77

East C1 no 39.60 -1.28 13 77% -6.58 0.00 -1.70 -0.86

East C1 no2 19.20 -0.02 13 0% -0.22 0.83 -0.24 0.20

Clinton C403 nox 57.13 -1.04 16 64% -5.36 0.00 -1.45 -0.63

Clinton C403 no 31.90 -0.35 16 26% -2.38 0.03 -0.66 -0.04

Clinton C403 no2 25.02 -0.62 16 62% -5.06 0.00 -0.89 -0.36

Crawford C407 nox 80.00 -2.64 9 69% -4.49 0.00 -3.96 -1.31

Crawford C407 no 51.46 -1.87 9 59% -3.62 0.01 -3.03 -0.70

Crawford C407 no2 29.96 -0.82 9 58% -3.5 0.01 -1.34 -0.29

Deer Park 2 C35 nox 26.00 -0.52 9 40% -2.45 0.04 -1.00 -0.04

Deer Park 2 C35 no 8.22 -0.14 9 17% -1.37 0.20 -0.38 0.09

Deer Park 2 C35 no2 16.54 -0.27 9 37% -2.3 0.05 -0.53 -0.01

57
The trends for other parts of the state are presented in Table 7.2. Time series graphs for the 

annual averages of all sites for the three parameter with the OLS lines fit to the data appear in 

Figures 7.2 – 7.69. 

Table 7.2 Ordinary least squares trends for 5-8 CST mean NOx, NO, and NO2 in other Texas Regions 

Site Description Species Intrcpt Slope DoF Rsq T p-val L95B U95B

Hinton St. C401 nox 58.38 -1.51 15 65% -5.26 0.00 -2.12 -0.90

Hinton St. C401 no 38.44 -1.63 15 66% -5.38 0.00 -2.28 -0.98

Hinton St. C401 no2 21.38 0.00 15 0% 0.00 1.00 -0.24 0.24

Executive Apt C402 nox 28.75 -0.48 11 41% -2.78 0.02 -0.87 -0.10

Executive Apt C402 no 11.96 -0.37 11 41% -2.78 0.02 -0.66 -0.08

Executive Apt C402 no2 17.13 -0.14 11 13% -1.30 0.22 -0.37 0.09

Denton Apt So C56 nox 8.82 0.41 8 19% 1.38 0.20 -0.28 1.10

Denton Apt So C56 no 1.97 0.19 8 19% 1.39 0.20 -0.13 0.51

Denton Apt So C56 no2 6.02 0.28 8 27% 1.70 0.13 -0.10 0.67

Ft. Worth NW C13 nox 26.35 0.08 16 1% 0.43 0.68 -0.32 0.48

Ft. Worth NW C13 no 10.41 0.10 16 5% 0.94 0.36 -0.12 0.31

Ft. Worth NW C13 no2 15.49 0.04 16 1% 0.31 0.76 -0.23 0.31

UTEP C12 nox 47.60 -0.97 16 61% -5.00 0.00 -1.38 -0.56

UTEP C12 no 22.51 -0.52 16 52% -4.18 0.00 -0.78 -0.26

UTEP C12 no2 24.68 -0.40 16 44% -3.58 0.00 -0.64 -0.16

Chamizal C41 nox 55.44 -1.18 8 32% -1.95 0.09 -2.57 0.22

Chamizal C41 no 29.82 -0.75 8 39% -2.26 0.05 -1.51 0.02

Chamizal C41 no2 27.84 -0.57 8 22% -1.50 0.17 -1.44 0.31

Beau-Lamar C2 nox 18.94 -0.22 16 24% -2.27 0.04 -0.43 -0.01

Beau-Lamar C2 no 8.98 -0.31 16 58% -4.73 0.00 -0.44 -0.17

Beau-Lamar C2 no2 9.58 0.11 16 8% 1.20 0.25 -0.09 0.31

West Orange C9 nox 20.33 -0.51 16 59% -4.77 0.00 -0.74 -0.28

West Orange C9 no 7.79 -0.21 16 36% -2.99 0.01 -0.36 -0.06

West Orange C9 no2 12.41 -0.28 16 57% -4.65 0.00 -0.41 -0.15

Audubon C38 nox 4.06 0.00 9 0% 0.00 1.00 -0.27 0.27

Audubon C38 no 0.65 -0.01 9 1% -0.24 0.81 -0.11 0.09

Audubon C38 no2 5.29 -0.12 9 16% -1.31 0.22 -0.32 0.09

SA Downtown C27 nox 52.86 -1.62 9 62% -3.85 0.00 -2.57 -0.67

SA Downtown C27 no 26.74 -0.96 9 58% -3.51 0.01 -1.57 -0.34

SA Downtown C27 no2 27.31 -0.73 9 59% -3.63 0.01 -1.18 -0.27

Calaveras Lake C59 nox 7.28 0.03 8 0% 0.20 0.85 -0.32 0.38

Calaveras Lake C59 no 2.65 0.01 8 0% 0.07 0.94 -0.16 0.17

Calaveras Lake C59 no2 6.53 -0.10 8 14% -1.15 0.28 -0.31 0.10

58
Figure 7.2 NO2 5-8 CST May-Oct. OLS Trends, years since 1990, Galveston Airport C34 

Figure 7.3 NO 5-8 CST May-Oct. OLS Trends, years since 1990, Galveston Airport C34 

Figure 7.4 NOx 5-8 CST May-Oct. OLS Trends, years since 1990, Galveston Airport C34 

59
Figure 7.5 NO2 5-8 CST May-Oct. OLS Trends, years since 1990, Aldine C8 

Figure 7.6 NO 5-8 CST May-Oct. OLS Trends, years since 1990, Aldine C8 

Figure 7.7 NOx 5-8 CST May-Oct. OLS Trends, years since 1990, Aldine C8 

60
Figure 7.8 NO2 5-8 CST May-Oct. OLS Trends, years since 1990, NW Harris C26 

Figure 7.9 NO 5-8 CST May-Oct. OLS Trends, years since 1990, NW Harris C26 

Figure 7.10 NOx 5-8 CST May-Oct. OLS Trends, years since 1990, NW Harris C26 

61
Figure 7.11 NO2 5-8 CST May-Oct. OLS Trends, years since 1990, Lang C408 

Figure 7.12 NO 5-8 CST May-Oct. OLS Trends, years since 1990, Lang C408 

Figure 7.13 NOx 5-8 CST May-Oct. OLS Trends, years since 1990, Lang C408 

62
Figure 7.14 NO2 5-8 CST May-Oct. OLS Trends, years since 1990, Lang C408 

Figure 7.15 NO 5-8 CST May-Oct. OLS Trends, years since 1990, Lang C408 

Figure 7.16 NOx 5-8 CST May-Oct. OLS Trends, years since 1990, Lang C408 

63
Figure 7.17 NO2 5-8 CST May-Oct. OLS Trends, years since 1990, East C1 

Figure 7.18 NO 5-8 CST May-Oct. OLS Trends, years since 1990, East C1 

Figure 7.19 NOx 5-8 CST May-Oct. OLS Trends, years since 1990, East C1 

64
Figure 7.20 NO2 5-8 CST May-Oct. OLS Trends, years since 1990, Clinton C403 

Figure 7.21 NO 5-8 CST May-Oct. OLS Trends, years since 1990, Clinton C403 

Figure 7.22 NOx 5-8 CST May-Oct. OLS Trends, years since 1990, Clinton C403 

65
Figure 7.23 NO2 5-8 CST May-Oct. OLS Trends, years since 1990, Crawford C407 

Figure 7.24 NO 5-8 CST May-Oct. OLS Trends, years since 1990, Crawford C407 

Figure 7.25 NOx 5-8 CST May-Oct. OLS Trends, years since 1990, Crawford C407 

66
Figure 7.26 NO2 5-8 CST May-Oct. OLS Trends, years since 1990, Deer Park C35 

Figure 7.27 NO 5-8 CST May-Oct. OLS Trends, years since 1990, Deer Park C35 

Figure 7.28 NOx 5-8 CST May-Oct. OLS Trends, years since 1990, Deer Park C35 

67
Figure 7.29 

Figure 7.30 

Figure 7.31 

68
Figure 7.32 

Figure 7.33 

Figure 7.34 

69
Figure 7.35 

Figure 7.36 

Figure 7.37 

70
Figure 7.38 

Figure 7.39 

Figure 7.40 

71
Figure 7.41 

Figure 7.42 

Figure 7.43 

72
Figure 7.44 

Figure 7.45 

Figure 7.46 

73
Figure 7.47 

Figure 7.48 

Figure 7.49 

74
Figure 7.50 

Figure 7.51 

Figure 7.52 

75
Figure 7.53 

Figure 7.54 

Figure 7.55 

76
Figure 7.56 

Figure 7.57 

Figure 7.58 

77
Figure 7.59 

Figure 7.60 

Figure 7.61 

78
8. Carbon Monoxide (CO) Trends 

8.1 Ordinary Least Squares Trends for CO 

As was discussed above for NOx, several representative monitors were selected to assess the 

diurnal patterns for CO trends. Figure 8.1 below shows the result. Interestingly, the relative 

magnitude of the morning and evening peaks varies by monitor. 

Figure 8.1 Pooled diurnal pattern from several representative monitors 

As was the case with NOx, the four morning hours beginning at 5 – 8 CST have been used to 

calculate an average to represent each day, then averaged over the year. Unlike NOx, the entire 

calendar year has been used to assess CO trends. 

79
Although the OLS trend graphs following are not arranged by region and are not labeled the 

same way the NOx graphs are, these will be cleaned up in the next revision of this report. The 

clear pattern of concentration decreases in all areas at all monitors shows the clear success of 

emission controls. Historical analysis of comparisons of CO and hydrocarbons measured at 

auto-GCs has shown high correlations. If CO has come down to the extent it has and the linear 

relationship still exists, then it is likely that hydrocarbon concentrations have declined as well. 

The summary of regression results appears in Table 8.1. Only the Deer Park site is not 

statistically significant at a 0.05 level, but it is close (p=0.06). 

Table 8.1 OLS regrssion results for CO, arranged by TCEQ region 

Site Description Incpt Slope DoF Rsq T P-val L95B U95B 

Hinton St. C401 604.75 -8.35 14 55% -4.13 0.00 -12.69 -4.01

Ft. Worth NW C13 1109.39 -48.60 16 91% -12.38 0.00 -56.92 -40.28

Tillman C413 1286.63 -52.63 13 76% -6.46 0.00 -70.24 -35.02

Downtown C6 2457.08 -138.29 11 91% -10.88 0.00 -166.27 -110.31

Ivanhoe C414 1253.55 -55.45 12 95% -14.43 0.00 -63.83 -47.08

UTEP C12 1255.26 -60.01 16 85% -9.57 0.00 -73.31 -46.71

Chamizal C41 1601.47 -63.76 15 91% -12.28 0.00 -74.83 -52.69

Sun Metro C40 2100.32 -92.52 9 93% -11.34 0.00 -110.98 -74.06

Austin NW C3 544.39 -23.61 9 68% -4.36 0.00 -35.85 -11.37

Aldine C8 1310.13 -57.06 16 71% -6.22 0.00 -76.51 -37.62

Lang C408 1702.72 -77.54 16 92% -13.40 0.00 -89.81 -65.27

Clinton C403 1058.96 -45.48 16 71% -6.25 0.00 -60.91 -30.05

Crawford C407 1701.09 -96.04 9 85% -7.08 0.00 -126.73 -65.36

DeerPrk 2 C35 700.15 -17.59 9 34% -2.18 0.06 -35.88 0.70

SA Downtown C27 1308.87 -49.81 14 75% -6.40 0.00 -66.50 -33.12

Brownsville C80 792.29 -21.59 12 48% -3.31 0.01 -35.78 -7.39

Laredo C44 909.12 -29.15 9 63% -3.90 0.00 -46.04 -12.25

80
Figure 8.2 CO 5-8 CST May-Oct. OLS Trends, years since 1990, Austin NW C3 

Figure 8.3 CO 5-8 CST May-Oct. OLS Trends, years since 1990, El Paso Ivanhoe NW C414 

81
Figure 8.4 CO 5-8 CST May-Oct. OLS Trends, years since 1990, El Paso Tillman C413 

Figure 8.4 CO 5-8 CST May-Oct. OLS Trends, years since 1990, Houston Lang C408 

82
Figure 8.5 CO 5-8 CST May-Oct. OLS Trends, years since 1990, Houston Crawford C407 

Figure 8.6 CO 5-8 CST May-Oct. OLS Trends, years since 1990, Houston Clinton C403 

83
Figure 8.7 CO 5-8 CST May-Oct. OLS Trends, years since 1990, Dallas Hinton C401 

Figure 8.8 CO 5-8 CST May-Oct. OLS Trends, years since 1990, Brownsville C80

84
Figure 8.9 CO 5-8 CST May-Oct. OLS Trends, years since 1990, Laredo C44 

Figure 8.10 CO 5-8 CST May-Oct. OLS Trends, years since 1990, El Paso Chamizal C41 

85
Figure 8.11 CO 5-8 CST May-Oct. OLS Trends, years since 1990, El Paso Sun Metro C40 

Figure 8.12 CO 5-8 CST May-Oct. OLS Trends, years since 1990, Houston Deer Park C35 

86
Figure 8.13 CO 5-8 CST May-Oct. OLS Trends, years since 1990, San Antonio Downtown C27 

Figure 8.14 CO 5-8 CST May-Oct. OLS Trends, years since 1990, Fort Worth NW C13 

87
Figure 8.15 CO 5-8 CST May-Oct. OLS Trends, years since 1990, El Paso UTEP C12 

Figure 8.16 CO 5-8 CST May-Oct. OLS Trends, years since 1990, Houston Aldine C8 

88
Figure 8.17 El Paso Downtown C6 

89
9. Sulfur dioxide (SO2) trends 

There are both national and state standards for sulfur dioxide (SO2). The NAAQS are 140 ppb 

for 24-hours and 30 ppb for an annual mean. Texas has a 30 minute standard of 400 ppb for 

residential areas but is constructed based on differencing upwind and downwind concentrations 

near a facility; in other words, no individual facility can be responsible for having contributed 

400 ppb to whatever downwind measurement is made. Thus, the state standard is almost always 

handled by mobile monitoring and not with CAMS. SO2 is a highly episodic pollutant, and 

although like other primary pollutants concentrations may be high at night, the mean diurnal 

patterns vary from site to site and provide little systematic clues as to a best time period to select 

for trend analysis. Thus, the simple 24-hour average has been chosen. 

90
9.1 SO2 ordinary least squares trends 

Table 9.1 SO2 OLS trend for daily 1hr max and daily mean 

Site 

Description Variable Incpt Slope DoF Rsq T 

pvalue L95B U95B

Hinton StC401 max 8.71 -0.46 14 65% -5.14 0.00 -0.65 -0.27

Hinton S C401 avg 3.06 -0.17 14 57% -4.34 0.00 -0.26 -0.09

Mid Towr C94 max 7.54 -0.09 9 10% -0.99 0.35 -0.31 0.12

Mid Towr C94 avg 1.62 -0.03 9 25% -1.72 0.12 -0.08 0.01

Mid OFW C52 max 27.77 -1.01 10 23% -1.72 0.12 -2.32 0.30

Mid OFW C52 avg 4.96 -0.20 10 30% -2.09 0.06 -0.42 0.01

UTEP C12 max 66.62 -4.72 16 85% -9.70 0.00 -5.75 -3.69

UTEP C12 avg 11.37 -0.76 16 85% -9.66 0.00 -0.93 -0.60

Sun Metro C40 max 28.32 -1.66 9 53% -3.16 0.01 -2.85 -0.47

Sun Metro C40 avg 7.08 -0.38 9 52% -3.11 0.01 -0.66 -0.11

TX Cty C10 max 26.29 -0.90 12 29% -2.23 0.05 -1.79 -0.02

TX Cty C10 avg 6.61 -0.18 12 8% -1.02 0.33 -0.56 0.20

Wayside C405 max 16.62 -0.67 16 86% -9.87 0.00 -0.82 -0.53

Wayside C405 avg 5.08 -0.21 16 73% -6.57 0.00 -0.27 -0.14

Croquet C409 max 9.90 -0.31 15 74% -6.45 0.00 -0.41 -0.21

Croquet C409 avg 2.83 -0.11 15 65% -5.28 0.00 -0.16 -0.07

Monroe C406 max 13.11 -0.35 16 65% -5.41 0.00 -0.48 -0.21

Monroe C406 avg 4.18 -0.11 16 37% -3.08 0.01 -0.18 -0.03

Clinton C403 max 27.16 -0.96 16 68% -5.88 0.00 -1.30 -0.61

Clinton C403 avg 5.64 -0.06 16 5% -0.92 0.37 -0.20 0.08

Bea-Lamar C2 max 30.36 -1.02 16 62% -5.09 0.00 -1.45 -0.60

Bea-Lamar C2 avg 7.62 -0.32 16 74% -6.67 0.00 -0.43 -0.22

PA West C28 max 25.84 -0.97 16 60% -4.85 0.00 -1.39 -0.55

PA West C28 avg 5.68 -0.25 16 55% -4.43 0.00 -0.37 -0.13

CarrollSt C54 max 42.12 -1.71 8 65% -3.88 0.00 -2.72 -0.69

CarrollSt C54 avg 10.77 -0.53 8 69% -4.21 0.00 -0.83 -0.24

West C4 max 10.86 -0.52 16 58% -4.67 0.00 -0.76 -0.28

West C4 avg 1.97 -0.06 16 18% -1.88 0.08 -0.13 0.01

Tuloso C21 max 8.39 -0.47 15 87% -9.90 0.00 -0.57 -0.37

Tuloso C21 avg 1.92 -0.10 15 48% -3.75 0.00 -0.16 -0.05

Huisache C98 max 16.13 -0.69 8 49% -2.76 0.02 -1.27 -0.11

Huisache C98 avg 2.87 -0.11 8 23% -1.55 0.16 -0.26 0.05

91
Figure 9.1 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Houston Croquet C409 

Figure 9.2 SO2 Annual Mean OLS Trends, years since 1990, Houston Croquet C409 

92
Figure 9.3 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Beaumont-Lamar C2 

Figure 9.4 SO2 Annual Mean OLS Trends, years since 1990, Beaumont-Lamar C2 

93
Figure 9.5 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Corpus Christi West C4 

Figure 9.6 SO2 Annual Mean OLS Trends, years since 1990, Corpus Christi West C4 

94
Figure 9.7 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Texas City C10 

Figure 9.8 SO2 Annual Mean OLS Trends, years since 1990, Texas City C10 

95
Figure 9.9 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, El Paso UTEP C12 

Figure 9.10 SO2 Annual Mean OLS Trends, years since 1990, El Paso UTEP C12 

96
Figure 9.11 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Corpus Christi Tuloso C21 

Figure 9.12 SO2 Annual Mean OLS Trends, years since 1990, Corpus Christi Tuloso C21 

97
Figure 9.13 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Port Arthur West C28 

Figure 9.14 SO2 Annual Mean OLS Trends, years since 1990, Port Arthur West C28 

98
Figure 9.15 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, El Paso Sun Metro C40 

Figure 9.16 SO2 Annual Mean OLS Trends, years since 1990, El Paso Sun Metro C40 

99
Figure 9.17 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Midlothian OFW C52 

Figure 9.18 SO2 Annual Mean OLS Trends, years since 1990, Midlothian OFW C52 

100
Figure 9.19 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Carroll St Park C54 

Figure 9.20 SO2 Annual Mean OLS Trends, years since 1990, Carroll St Park C54 

101
Figure 9.21 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Midlothian Tower C94 

Figure 9.22 SO2 Annual Mean OLS Trends, years since 1990, Midlothian Tower C94 

102
Figure 9.23 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Corpus Christi Huisache C98 

Figure 9.24 SO2 Annual Mean OLS Trends, years since 1990, Corpus Christi Huisache C98 

103
Figure 9.25 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Dallas Hinton C401 

Figure 9.26 SO2 Annual Mean OLS Trends, years since 1990, Dallas Hinton C401 

104
Figure 9.27 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Houston Clinton C403 

Figure 9.28 SO2 Annual Mean OLS Trends, years since 1990, Houston Clinton C403 

105
Figure 9.29 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Houston North Wayside C405 

Figure 9.30 SO2 Annual Mean OLS Trends, years since 1990, Houston North Wayside C405 

106
Figure 9.31 SO2 Daily One-Hour Peak Mean OLS Trends, years since 1990, Houston Monroe C406 

Figure 9.32 SO2 Annual Mean OLS Trends, years since 1990, Houston Monroe C406 

107
10. Hydrocarbon Species Trends (HRVOC, BTEX, Significant Alkane 

Compounds) 

Only four auto-GCs operating in Texas have data for more than 10 years. These are: 

481130069 Dallas Hinton Street CAMS 401 

481410044 El Paso Chamizal CAMS 41 

482011035 Houston Clinton Drive CAMS 403 

482011039 Deer Park CAMS 35 

Data were examined from May – October for all years since 1995. Deer Park began in 1997 and 

lacks “complete” data for three years since then. In an initial analysis, a 75 percent completeness 

by year was required, and this was lowered to 50 percent to try to increase data inclusion for 

Deer Park. This lowering of completeness had negligible affect on the trends and did have a 

slight increase in including missing years for other sites, but did not help Deer Park. 

10.1 Ordinary Least Squares Trends for HC 

Several species from the four long-running auto-GCs in Dallas, Houston, and El Paso have been 

selected for analysis. These species represent three classes of hydrocarbons: 

• Highly reactive species: ethylene, propylene, 1-butene 

• “BTEX” substituted aromatics: benzene, toluene, ethyl benzene, m/p-xylene, and oxylene 

• Significant alkanes: n-butane, isobutane, n-pentane, isopentane 

The OLS trends for these 12 species are summarized in Table 10.1 and graphed in Figures 10.1 – 

10.48. 

108
Table 10.1 

Site Description species Intercept slope DoF R2 T p-value L95B U95B

Hinton St. C401 Ethylene 8.55 -0.39 10 0.797 -6.27 0.00 -0.53 -0.25

Hinton St. C401 Propylene 3.67 -0.11 10 0.463 -2.94 0.01 -0.19 -0.03

Hinton St. C401 1-Butene 0.58 -0.01 10 0.279 -1.97 0.08 -0.03 0.00

Hinton St. C401 Benzene 4.30 -0.19 10 0.634 -4.17 0.00 -0.29 -0.09

Hinton St. C401 Toluene 14.20 -0.44 10 0.366 -2.40 0.04 -0.85 -0.03

Hinton St. C401 m/p-Xylene 10.67 -0.54 10 0.765 -5.71 0.00 -0.75 -0.33

Hinton St. C401 o-Xylene 3.59 -0.18 10 0.730 -5.20 0.00 -0.25 -0.10

Hinton St. C401 Ethyl Benzene 3.05 -0.15 10 0.744 -5.39 0.00 -0.21 -0.09

Hinton St. C401 n-Butane 10.30 -0.32 10 0.504 -3.18 0.01 -0.54 -0.09

Hinton St. C401 Isobutane 3.85 -0.05 10 0.167 -1.41 0.19 -0.12 0.03

Hinton St. C401 n-Pentane 8.21 -0.31 10 0.422 -2.70 0.02 -0.57 -0.05

Hinton St. C401 Isopentane 16.60 -0.68 10 0.634 -4.16 0.00 -1.05 -0.32

Chamizal C41 Ethylene 16.79 -0.78 11 0.743 -5.64 0.00 -1.08 -0.47

Chamizal C41 Propylene 7.65 -0.31 11 0.680 -4.84 0.00 -0.45 -0.17

Chamizal C41 1-Butene 1.28 -0.04 11 0.402 -2.72 0.02 -0.08 -0.01

Chamizal C41 Benzene 13.59 -0.64 11 0.797 -6.56 0.00 -0.85 -0.42

Chamizal C41 Toluene 34.48 -1.54 11 0.841 -7.62 0.00 -1.98 -1.09

Chamizal C41 m/p-Xylene 21.27 -1.05 11 0.910 -10.55 0.00 -1.27 -0.83

Chamizal C41 o-Xylene 8.13 -0.41 11 0.889 -9.41 0.00 -0.50 -0.31

Chamizal C41 Ethyl Benzene 6.42 -0.30 11 0.790 -6.44 0.00 -0.40 -0.20

Chamizal C41 n-Butane 16.91 -0.66 11 0.493 -3.27 0.01 -1.10 -0.21

Chamizal C41 Isobutane 5.40 -0.12 11 0.208 -1.70 0.12 -0.28 0.04

Chamizal C41 n-Pentane 16.42 -0.60 11 0.549 -3.66 0.00 -0.95 -0.24

Chamizal C41 Isopentane 23.80 -0.80 11 0.528 -3.51 0.00 -1.30 -0.30

Clinton C403 Ethylene 21.51 -0.87 10 0.710 -4.95 0.00 -1.27 -0.48

Clinton C403 Propylene 17.71 -0.69 10 0.852 -7.59 0.00 -0.89 -0.49

Clinton C403 1-Butene 3.45 -0.10 10 0.237 -1.76 0.11 -0.22 0.03

Clinton C403 Benzene 9.73 -0.41 10 0.719 -5.06 0.00 -0.58 -0.23

Clinton C403 Toluene 20.89 -0.85 10 0.693 -4.75 0.00 -1.25 -0.45

Clinton C403 m/p-Xylene 14.45 -0.66 10 0.756 -5.57 0.00 -0.93 -0.40

Clinton C403 o-Xylene 5.02 -0.23 10 0.789 -6.12 0.00 -0.31 -0.14

Clinton C403 Ethyl Benzene 4.19 -0.18 10 0.614 -3.98 0.00 -0.27 -0.08

Clinton C403 n-Butane 32.84 -0.70 10 0.212 -1.64 0.13 -1.65 0.25

Clinton C403 Isobutane 34.18 -0.83 10 0.037 -0.62 0.55 -3.82 2.16

Clinton C403 n-Pentane 16.51 -0.34 10 0.500 -3.16 0.01 -0.58 -0.10

Clinton C403 Isopentane 32.59 -0.63 10 0.490 -3.10 0.01 -1.09 -0.18

DeerPark C35 Ethylene 29.72 -1.13 7 0.531 -2.82 0.03 -2.07 -0.18

DeerPark C35 Propylene 39.68 -1.36 8 0.334 -2.00 0.08 -2.93 0.20

DeerPark C35 1-Butene 2.69 -0.05 8 0.046 -0.62 0.55 -0.26 0.15

DeerPark C35 Benzene 6.01 -0.03 8 0.006 -0.21 0.84 -0.37 0.31

DeerPark C35 Toluene 10.47 -0.17 8 0.110 -0.99 0.35 -0.55 0.22

DeerPark C35 m/p-Xylene 4.78 -0.10 8 0.199 -1.41 0.20 -0.26 0.06

DeerPark C35 o-Xylene 1.88 -0.05 8 0.315 -1.92 0.09 -0.10 0.01

DeerPark C35 Ethyl Benzene 1.87 -0.05 8 0.328 -1.98 0.08 -0.10 0.01

DeerPark C35 n-Butane 15.85 0.07 8 0.006 0.22 0.83 -0.66 0.80

DeerPark C35 Isobutane 23.78 -0.45 8 0.215 -1.48 0.18 -1.15 0.25

DeerPark C35 n-Pentane 8.26 0.09 8 0.026 0.47 0.65 -0.34 0.52

DeerPark C35 Isopentane 17.18 0.00 8 0.000 -0.01 0.99 -0.70 0.70

109
Figure 10.1 1-Butene 6-9 CST May-Oct. OLS Trend, years since 1990, Deer Park C35 

Figure 10.2 Benzene 6-9 CST May-Oct. OLS Trend, years since 1990, Deer Park C35 

110
Figure 10.3 Ethyl Benzene 6-9 CST May-Oct. OLS Trend, years since 1990, Deer Park C35 

Figure 10.4 Ethylene 6-9 CST May-Oct. OLS Trend, years since 1990, Deer Park C35 

111
Figure 10.5 Isobutane 6-9 CST May-Oct. OLS Trend, years since 1990, Deer Park C35 

Figure 10.6 Isopentane 6-9 CST May-Oct. OLS Trend, years since 1990, Deer Park C35 

112
Figure 10.7 m/p-Xylene 6-9 CST May-Oct. OLS Trend, years since 1990, Deer Park C35 

Figure 10.8 n-Pentane 6-9 CST May-Oct. OLS Trend, years since 1990, Deer Park C35 

113
Figure 10.9 n-Butane 6-9 CST May-Oct. OLS Trend, years since 1990, Deer Park C35 

Figure 10.10 o-Xylene 6-9 CST May-Oct. OLS Trend, years since 1990, Deer Park C35 

114
Figure 10.11 Propylene 6-9 CST May-Oct. OLS Trend, years since 1990, Deer Park C35 

Figure 10.12 Toluene 6-9 CST May-Oct. OLS Trend, years since 1990, Deer Park C35 

115
Figure 10.13 1-Butene 6-9 CST May-Oct. OLS Trend, years since 1990, Chamizal C41 

Figure 10.14 Benzene 6-9 CST May-Oct. OLS Trend, years since 1990, Chamizal C41 

116
Figure 10.15 Ethyl Benzene 6-9 CST May-Oct. OLS Trend, years since 1990, Chamizal C41 

Figure 10.16 Ethylene 6-9 CST May-Oct. OLS Trend, years since 1990, Chamizal C41 

117
Figure 10.17 Isobutane 6-9 CST May-Oct. OLS Trend, years since 1990, Chamizal C41 

Figure 10.18 Isopentane 6-9 CST May-Oct. OLS Trend, years since 1990, Chamizal C41 

118
Figure 10.19 m/p-Xylene 6-9 CST May-Oct. OLS Trend, years since 1990, Chamizal C41 

Figure 10.20 n-Butane 6-9 CST May-Oct. OLS Trend, years since 1990, Chamizal C41 

119
Figure 10.21n-Pentane 6-9 CST May-Oct. OLS Trend, years since 1990, Chamizal C41 

Figure 10.22 o-Xylene 6-9 CST May-Oct. OLS Trend, years since 1990, Chamizal C41 

120
Figure 10.23 Propylene 6-9 CST May-Oct. OLS Trend, years since 1990, Chamizal C41 

Figure 10.24 Toluene 6-9 CST May-Oct. OLS Trend, years since 1990, Chamizal C41 

121
Figure 10.25 1-Butene 6-9 CST May-Oct. OLS Trend, years since 1990, Dallas Hinton C401 

Figure 10.26 Benzene 6-9 CST May-Oct. OLS Trend, years since 1990, Dallas Hinton C401 

122
Figure 10.27 Ethyl Benzene 6-9 CST May-Oct. OLS Trend, years since 1990, Dallas Hinton C401 

Figure 10.28 Ethylene 6-9 CST May-Oct. OLS Trend, years since 1990, Dallas Hinton C401 

123
Figure 10.29 isobutane 6-9 CST May-Oct. OLS Trend, years since 1990, Dallas Hinton C401 

Figure 10.30 Isopentane 6-9 CST May-Oct. OLS Trend, years since 1990, Dallas Hinton C401 

124
Figure 10.31 m/p-Xylene 6-9 CST May-Oct. OLS Trend, years since 1990, Dallas Hinton C401 

Figure 10.32 n-Butane 6-9 CST May-Oct. OLS Trend, years since 1990, Dallas Hinton C401 

125
Figure 10.33 n-Pentane 6-9 CST May-Oct. OLS Trend, years since 1990, Dallas Hinton C401 

Figure 10.34 o-Xylene 6-9 CST May-Oct. OLS Trend, years since 1990, Dallas Hinton C401 

126
Figure 10.35 Propylene 6-9 CST May-Oct. OLS Trend, years since 1990, Dallas Hinton C401 

Figure 10.36 Toluene 6-9 CST May-Oct. OLS Trend, years since 1990, Dallas Hinton C401 

127
Figure 10.37 1-Butene 6-9 CST May-Oct. OLS Trend, years since 1990, Clinton Dr. C403 

Figure 10.38 Benzene 6-9 CST May-Oct. OLS Trend, years since 1990, Clinton Dr. C403 

128
Figure 10.39 Ethyl Benzene 6-9 CST May-Oct. OLS Trend, years since 1990, Clinton Dr. C403 

Figure 10.40 Ethylene 6-9 CST May-Oct. OLS Trend, years since 1990, Clinton Dr. C403 

129
Figure 10.41 Isobutane 6-9 CST May-Oct. OLS Trend, years since 1990, Clinton Dr. C403 

Figure 10.42 Isopentane 6-9 CST May-Oct. OLS Trend, years since 1990, Clinton Dr. C403 

130
Figure 10.43 m/p-Xylene 6-9 CST May-Oct. OLS Trend, years since 1990, Clinton Dr. C403 

Figure 10.44 n-Butane 6-9 CST May-Oct. OLS Trend, years since 1990, Clinton Dr. C403 

131
Figure 10.45 n-Pentane 6-9 CST May-Oct. OLS Trend, years since 1990, Clinton Dr. C403 

Figure 10.46 o-Xylene 6-9 CST May-Oct. OLS Trend, years since 1990, Clinton Dr. C403 

132
Figure 10.47 Propylene 6-9 CST May-Oct. OLS Trend, years since 1990, Clinton Dr. C403 

Figure 10.48 Toluene 6-9 CST May-Oct. OLS Trend, years since 1990, Clinton Dr. C403 

133
10.2 Meteorologically-adjusted Trends for HCs 

Tables 10.2 – 10.5 show the Pearson correlations for the regional meteorological data with the 

natural logarithm of the morning average for the four PAMS monitoring sites with long term 

data. Correlations are color-coded such that correlations greater than or equal to 0.3 are orange, 

correlations greater than or equal to 0.6 are red, and less than or equal to -0.4 are blue. The 

results appear in the table for each of the regional meteorological parameters. 

A similar analysis was conducted using Spearman rank-based correlations were also assessed 

and found to have nearly identical results, suggesting the skewness in the data are not a major 

issue. 

The last row in Tables 10.2 – 10.5 is the variable “yr”. This is a variable for the year, and the 

correlation reflects whether a species mean morning concentration tended to increase or decrease 

by year. In most cases in the four tables, the value is negative, indicating a tendency for lower 

concentrations. 

The specific set of parameters having the highest absolute values in correlations have been 

selected for use as independent variables in the meteorological adjusted trends. 

134
135

Table 10.2 Correlations for morning hydrocarbons species with METDAT variables at Hinton Street DFW 

481130069 

1_Butene 

Benzene 

Ethyl 

benzene 

Ethylene 

Isobutane Isopentan 

Propylene 

Toluene 

Butane 

Pentane 

o-Xylene 

mp_xylen 

CCAVGPM -0.15 -0.12 -0.13 -0.14 -0.13 -0.21 -0.15 -0.14 -0.14 -0.21 -0.13 -0.13

CCAVTAM -0.27 -0.23 -0.25 -0.25 -0.19 -0.31 -0.25 -0.27 -0.18 -0.29 -0.25 -0.25

MRMAX -0.20 -0.21 0.00 -0.18 -0.43 -0.17 -0.19 0.00 -0.55 -0.26 0.01 0.03

RAINHRS -0.09 -0.06 -0.10 -0.08 -0.04 -0.11 -0.09 -0.10 -0.03 -0.11 -0.10 -0.10

RHAVGMID -0.14 -0.08 -0.15 -0.09 -0.06 -0.17 -0.12 -0.17 -0.05 -0.16 -0.14 -0.14

D24T850 0.16 0.18 0.19 0.20 0.12 0.17 0.19 0.19 0.09 0.12 0.19 0.19

DEVH700 0.18 0.16 0.18 0.17 0.08 0.12 0.20 0.19 0.07 0.08 0.19 0.19

DEVH850 0.21 0.21 0.23 0.23 0.14 0.18 0.22 0.23 0.12 0.15 0.24 0.22

DEVT700 -0.01 -0.05 -0.03 -0.04 -0.08 -0.07 0.02 -0.03 -0.08 -0.10 -0.02 -0.01

DEVT850 0.02 -0.03 0.00 -0.03 -0.04 -0.01 0.02 0.01 -0.03 -0.04 0.01 0.02

DPAVG -0.15 -0.18 0.03 -0.14 -0.39 -0.14 -0.14 0.04 -0.52 -0.24 0.05 0.07

DPMAX -0.17 -0.19 0.01 -0.16 -0.39 -0.15 -0.16 0.02 -0.51 -0.24 0.03 0.05

DT700 0.20 0.29 0.08 0.27 0.40 0.19 0.27 0.06 0.50 0.25 0.08 0.06

DT850 0.23 0.30 0.13 0.29 0.38 0.24 0.28 0.12 0.47 0.27 0.13 0.12

DT925 0.42 0.49 0.41 0.51 0.50 0.50 0.49 0.43 0.52 0.48 0.42 0.40

H500 -0.06 -0.08 0.14 -0.03 -0.32 -0.03 -0.03 0.16 -0.44 -0.14 0.15 0.16

H700 0.00 -0.02 0.18 0.03 -0.25 0.02 0.03 0.20 -0.36 -0.09 0.20 0.20

H850 0.13 0.14 0.25 0.17 -0.01 0.16 0.15 0.26 -0.07 0.09 0.26 0.25

SLPAVG 0.19 0.21 0.10 0.20 0.27 0.16 0.18 0.08 0.34 0.21 0.10 0.07

STPAVG 0.12 0.20 0.11 0.18 0.23 0.16 0.15 0.10 0.29 0.19 0.11 0.09

T500 -0.12 -0.13 0.09 -0.08 -0.33 -0.05 -0.09 0.11 -0.45 -0.15 0.10 0.11

T700 -0.14 -0.18 0.02 -0.14 -0.40 -0.14 -0.11 0.03 -0.51 -0.24 0.03 0.05

T850 -0.13 -0.18 0.04 -0.15 -0.39 -0.12 -0.13 0.05 -0.50 -0.22 0.05 0.07

T925 -0.04 -0.09 0.14 -0.05 -0.30 0.00 -0.03 0.17 -0.42 -0.11 0.16 0.17

TAAVG -0.14 -0.19 0.06 -0.14 -0.42 -0.12 -0.14 0.08 -0.56 -0.23 0.07 0.09

TAMAX -0.09 -0.14 0.11 -0.09 -0.36 -0.05 -0.08 0.13 -0.50 -0.16 0.12 0.14

TAMIN -0.20 -0.25 0.00 -0.20 -0.47 -0.18 -0.21 0.01 -0.60 -0.29 0.00 0.03

TAVGAM -0.12 -0.18 0.07 -0.13 -0.41 -0.10 -0.12 0.09 -0.54 -0.21 0.09 0.10

TAVGPM -0.04 -0.10 0.14 -0.05 -0.32 -0.01 -0.04 0.16 -0.45 -0.12 0.15 0.17

TD850 -0.23 -0.23 -0.06 -0.21 -0.37 -0.20 -0.21 -0.05 -0.48 -0.28 -0.05 -0.04

TDELTA 0.35 0.34 0.34 0.34 0.31 0.40 0.36 0.36 0.32 0.39 0.34 0.34

TMAX -0.06 -0.12 0.13 -0.07 -0.34 -0.03 -0.06 0.15 -0.47 -0.14 0.13 0.15

TRANDIS -0.36 -0.47 -0.47 -0.46 -0.40 -0.54 -0.41 -0.50 -0.36 -0.48 -0.48 -0.46

TRANU 0.07 0.09 0.06 0.06 0.16 0.17 0.06 0.08 0.26 0.24 0.04 0.05

TRANV -0.09 -0.13 -0.04 -0.10 -0.26 -0.17 -0.07 -0.05 -0.31 -0.23 -0.04 -0.01

TRANW 0.17 0.18 0.09 0.15 0.34 0.25 0.16 0.11 0.43 0.32 0.07 0.06

WNDRUN -0.34 -0.45 -0.47 -0.45 -0.38 -0.53 -0.40 -0.50 -0.34 -0.48 -0.47 -0.46

UAVG -0.08 -0.14 -0.19 -0.15 0.00 -0.07 -0.14 -0.16 0.11 0.01 -0.21 -0.20

UAVGAM -0.09 -0.16 -0.16 -0.16 -0.06 -0.07 -0.15 -0.13 0.00 -0.01 -0.18 -0.17

UAVGPM -0.06 -0.11 -0.18 -0.12 0.03 -0.07 -0.11 -0.16 0.12 0.01 -0.19 -0.18

VAVG 0.00 -0.05 0.03 -0.01 -0.14 -0.08 0.03 0.03 -0.21 -0.12 0.04 0.05

VAVGAM -0.01 -0.03 0.02 0.00 -0.15 -0.08 0.03 0.02 -0.21 -0.13 0.03 0.05

VAVGPM 0.04 0.00 0.06 0.04 -0.09 -0.03 0.06 0.06 -0.14 -0.07 0.07 0.08

WSAVG -0.38 -0.40 -0.42 -0.43 -0.37 -0.50 -0.38 -0.47 -0.33 -0.43 -0.43 -0.41

WSAVGAM -0.41 -0.40 -0.44 -0.44 -0.35 -0.49 -0.40 -0.50 -0.29 -0.41 -0.45 -0.43

WSAVGPM -0.19 -0.22 -0.24 -0.23 -0.18 -0.28 -0.19 -0.28 -0.14 -0.22 -0.25 -0.23

yr 0.04 -0.32 -0.26 -0.30 -0.08 -0.31 -0.19 -0.19 -0.18 -0.25 -0.28 -0.29
136

Table 10.3 Correlations for morning hydrocarbons species with METDAT variables at Chamizal El Paso 

481410044 

1_Butene 

Benzene 

Ethyl 

benzene 

Ethylene 

Isobutane Isopentan 

Propylene 

Toluene 

Butane 

Pentane 

o-Xylen mp_xylen 

CCAVGPM -0.01 -0.05 -0.02 -0.05 0.02 -0.02 -0.04 -0.05 -0.01 -0.01 -0.04 -0.04

CCAVTAM -0.06 -0.11 -0.09 -0.09 -0.02 -0.08 -0.08 -0.11 -0.02 -0.07 -0.10 -0.10

MRMAX -0.09 -0.07 0.02 -0.15 -0.19 -0.03 -0.10 0.03 -0.30 -0.07 0.03 0.02

RAINHRS -0.08 -0.10 -0.09 -0.09 -0.09 -0.10 -0.09 -0.09 -0.08 -0.09 -0.07 -0.08

RHAVGMID -0.03 -0.08 -0.07 -0.02 0.00 -0.07 -0.05 -0.10 0.04 -0.05 -0.05 -0.05

D24T850 0.27 0.29 0.30 0.26 0.24 0.30 0.27 0.31 0.23 0.29 0.30 0.30

DEVH500 0.29 0.31 0.32 0.31 0.35 0.36 0.31 0.32 0.31 0.34 0.32 0.32

DEVH700 0.29 0.31 0.31 0.32 0.37 0.36 0.30 0.31 0.33 0.35 0.32 0.32

DEVH850 0.18 0.20 0.18 0.22 0.27 0.21 0.19 0.17 0.23 0.21 0.19 0.19

DEVT500 0.15 0.16 0.16 0.15 0.13 0.18 0.16 0.18 0.12 0.16 0.17 0.17

DEVT700 0.26 0.28 0.30 0.26 0.29 0.33 0.28 0.31 0.26 0.31 0.30 0.30

DEVT850 0.24 0.25 0.26 0.22 0.22 0.30 0.24 0.28 0.21 0.28 0.27 0.27

DPAVG -0.07 -0.06 0.04 -0.14 -0.16 -0.01 -0.08 0.04 -0.27 -0.05 0.05 0.04

DPMAX -0.08 -0.07 0.03 -0.16 -0.18 -0.02 -0.10 0.03 -0.29 -0.06 0.03 0.02

DT700 0.36 0.35 0.30 0.44 0.48 0.35 0.38 0.27 0.57 0.38 0.31 0.32

DT850 0.46 0.46 0.42 0.51 0.52 0.48 0.47 0.42 0.59 0.49 0.44 0.44

H700 0.12 0.16 0.23 0.09 0.11 0.23 0.13 0.24 -0.02 0.19 0.23 0.23

H850 0.19 0.19 0.19 0.22 0.27 0.23 0.19 0.18 0.23 0.23 0.20 0.20

MHMAX -0.36 -0.35 -0.31 -0.40 -0.47 -0.39 -0.35 -0.30 -0.51 -0.41 -0.33 -0.33

SLPAVG 0.16 0.12 0.07 0.23 0.30 0.13 0.15 0.03 0.36 0.17 0.08 0.08

STPAVG 0.10 0.09 0.10 0.12 0.19 0.13 0.09 0.07 0.14 0.14 0.10 0.09

T700 0.02 0.08 0.17 -0.04 -0.06 0.14 0.03 0.19 -0.19 0.08 0.16 0.15

T850 -0.01 0.04 0.13 -0.10 -0.12 0.10 -0.01 0.16 -0.26 0.04 0.12 0.12

TAAVG -0.12 -0.07 0.03 -0.21 -0.25 -0.03 -0.12 0.06 -0.40 -0.09 0.02 0.01

TAMAX -0.05 0.01 0.11 -0.13 -0.17 0.06 -0.04 0.14 -0.32 -0.01 0.09 0.09

TAMIN -0.19 -0.16 -0.06 -0.29 -0.33 -0.12 -0.21 -0.04 -0.47 -0.18 -0.07 -0.08

TAVGAM -0.13 -0.07 0.02 -0.22 -0.25 -0.03 -0.13 0.05 -0.40 -0.09 0.01 0.01

TAVGPM -0.04 0.02 0.11 -0.12 -0.16 0.07 -0.03 0.14 -0.31 0.00 0.10 0.09

TD850 -0.14 -0.14 -0.05 -0.22 -0.21 -0.10 -0.16 -0.06 -0.32 -0.13 -0.06 -0.06

TDELTA 0.37 0.44 0.42 0.41 0.41 0.44 0.41 0.44 0.40 0.43 0.41 0.42

TMAX -0.04 0.02 0.11 -0.13 -0.16 0.06 -0.04 0.14 -0.31 0.00 0.09 0.09

TRANDIS -0.32 -0.35 -0.36 -0.35 -0.33 -0.36 -0.33 -0.35 -0.32 -0.35 -0.36 -0.36

TRANU -0.13 -0.15 -0.16 -0.12 -0.26 -0.20 -0.12 -0.13 -0.16 -0.20 -0.16 -0.16

TRANV 0.14 0.15 0.17 0.13 0.06 0.15 0.16 0.19 0.05 0.13 0.18 0.18

TRANW 0.22 0.24 0.24 0.25 0.24 0.25 0.22 0.23 0.24 0.25 0.23 0.24

WNDRUN -0.32 -0.35 -0.36 -0.36 -0.32 -0.36 -0.34 -0.36 -0.31 -0.35 -0.36 -0.36

UAVG -0.19 -0.21 -0.23 -0.20 -0.30 -0.27 -0.20 -0.21 -0.22 -0.27 -0.22 -0.22

UAVGAM -0.26 -0.28 -0.29 -0.26 -0.36 -0.33 -0.27 -0.27 -0.29 -0.33 -0.29 -0.29

UAVGPM -0.08 -0.09 -0.11 -0.08 -0.15 -0.13 -0.07 -0.09 -0.08 -0.13 -0.11 -0.10

VAVG 0.14 0.14 0.18 0.10 0.03 0.14 0.14 0.19 0.02 0.12 0.17 0.17

VAVGAM 0.16 0.14 0.18 0.11 0.04 0.15 0.16 0.19 0.05 0.13 0.17 0.17

VAVGPM 0.13 0.15 0.17 0.11 0.08 0.14 0.15 0.18 0.06 0.12 0.17 0.16

WSAVG -0.34 -0.38 -0.38 -0.38 -0.42 -0.42 -0.36 -0.36 -0.38 -0.41 -0.37 -0.37

WSAVGAM -0.36 -0.39 -0.41 -0.38 -0.42 -0.44 -0.36 -0.39 -0.37 -0.43 -0.39 -0.40

WSAVGPM -0.16 -0.18 -0.19 -0.19 -0.23 -0.21 -0.18 -0.18 -0.20 -0.21 -0.19 -0.19

yr -0.23 -0.25 -0.23 -0.30 -0.07 -0.20 -0.27 -0.26 -0.16 -0.20 -0.32 -0.32
137

Table 10.4 Correlations for morning hydrocarbons species with METDAT variables at Clinton Dr. Houston 

482011035 

1_Butene Benzene Ethyl benzene Ethylene Isobutane Isopentan Propylene Toluene Butane Pentane o-Xylene mp_xylen 

CCAVGPM 0.14 -0.02 -0.09 -0.02 0.11 0.03 0.09 -0.08 0.03 0.04 -0.08 -0.06

CCAVTAM 0.03 -0.14 -0.20 -0.14 0.00 -0.10 -0.03 -0.21 -0.07 -0.08 -0.19 -0.17

MRMAX 0.17 0.00 0.17 0.02 0.02 0.08 0.12 0.13 -0.17 0.03 0.18 0.18

RAINHRS 0.01 -0.06 -0.14 -0.03 0.00 -0.04 0.01 -0.13 -0.02 -0.02 -0.12 -0.11

RHAVGMID 0.20 0.06 -0.04 0.07 0.16 0.10 0.18 -0.04 0.11 0.12 -0.02 0.01

D24T850 0.26 0.20 0.23 0.20 0.19 0.22 0.24 0.21 0.18 0.19 0.23 0.24

D24TD850 0.09 0.05 0.04 0.05 0.07 0.07 0.09 0.04 0.05 0.07 0.05 0.06

DEVH700 0.21 0.14 0.12 0.12 0.18 0.17 0.18 0.10 0.14 0.15 0.11 0.12

DEVH850 0.18 0.17 0.11 0.15 0.18 0.18 0.18 0.12 0.16 0.17 0.10 0.11

DEVT500 0.06 -0.03 0.00 -0.04 0.04 0.01 0.02 -0.02 0.01 0.00 0.00 0.00

DEVT700 0.15 0.01 0.06 -0.02 0.10 0.07 0.07 0.02 0.05 0.04 0.07 0.09

DEVT850 0.08 -0.03 0.04 -0.05 0.01 0.00 0.02 -0.01 -0.02 -0.01 0.05 0.06

DPAVG 0.23 0.05 0.21 0.07 0.07 0.13 0.17 0.17 -0.11 0.08 0.21 0.22

DPMAX 0.22 0.04 0.21 0.06 0.06 0.13 0.16 0.16 -0.11 0.07 0.21 0.21

DT700 0.07 0.18 0.04 0.16 0.18 0.12 0.11 0.06 0.31 0.15 0.03 0.04

DT850 0.02 0.16 0.05 0.14 0.10 0.08 0.09 0.06 0.21 0.10 0.04 0.05

DT925 0.13 0.27 0.22 0.25 0.18 0.21 0.21 0.23 0.25 0.21 0.21 0.21

H700 0.16 0.05 0.16 0.06 0.06 0.12 0.13 0.13 -0.09 0.06 0.14 0.14

H850 0.16 0.13 0.15 0.13 0.11 0.16 0.17 0.14 0.04 0.13 0.13 0.13

SLPAVG -0.11 0.03 -0.11 0.01 0.00 -0.02 -0.05 -0.07 0.11 0.01 -0.13 -0.14

STPAVG -0.11 0.03 -0.11 0.01 0.00 -0.02 -0.05 -0.07 0.11 0.01 -0.12 -0.13

T850 0.05 -0.09 0.09 -0.07 -0.07 -0.02 0.00 0.04 -0.25 -0.08 0.08 0.08

T925 0.09 -0.04 0.15 -0.03 -0.04 0.03 0.05 0.11 -0.21 -0.02 0.14 0.13

TAAVG 0.15 -0.01 0.20 0.01 0.00 0.07 0.10 0.15 -0.20 0.01 0.19 0.19

TAMAX 0.17 0.04 0.26 0.06 0.02 0.11 0.13 0.21 -0.16 0.04 0.25 0.25

TAMIN 0.11 -0.07 0.12 -0.05 -0.04 0.02 0.05 0.07 -0.24 -0.04 0.11 0.11

TAVGAM 0.17 0.01 0.22 0.03 0.02 0.10 0.11 0.18 -0.17 0.04 0.21 0.22

TAVGPM 0.15 0.05 0.27 0.06 0.00 0.10 0.11 0.23 -0.16 0.03 0.26 0.25

TD850 -0.07 -0.17 -0.08 -0.17 -0.13 -0.12 -0.10 -0.09 -0.26 -0.14 -0.08 -0.09

TDELTA 0.11 0.27 0.35 0.25 0.11 0.21 0.16 0.35 0.20 0.17 0.34 0.32

TMAX 0.16 0.04 0.27 0.06 0.00 0.11 0.11 0.23 -0.17 0.03 0.26 0.25

TRANDIS -0.09 -0.30 -0.37 -0.32 -0.09 -0.25 -0.22 -0.40 -0.12 -0.21 -0.34 -0.31

TRANU -0.30 -0.06 0.04 -0.08 -0.27 -0.18 -0.21 0.05 -0.17 -0.19 0.06 0.02

TRANV 0.35 0.12 0.26 0.11 0.18 0.19 0.23 0.18 0.07 0.14 0.28 0.31

TRANW -0.32 -0.19 -0.24 -0.19 -0.21 -0.22 -0.28 -0.20 -0.07 -0.18 -0.22 -0.23

WNDRUN -0.09 -0.30 -0.36 -0.32 -0.09 -0.24 -0.22 -0.40 -0.11 -0.21 -0.34 -0.30

UAVG -0.28 -0.18 -0.01 -0.21 -0.30 -0.23 -0.27 -0.04 -0.24 -0.25 0.02 -0.01

UAVGAM -0.26 -0.19 0.01 -0.22 -0.33 -0.24 -0.26 -0.03 -0.30 -0.29 0.04 0.02

UAVGPM -0.24 -0.19 -0.08 -0.22 -0.25 -0.23 -0.26 -0.11 -0.18 -0.23 -0.05 -0.07

VAVG 0.46 0.25 0.38 0.23 0.29 0.33 0.34 0.32 0.21 0.28 0.39 0.43

VAVGAM 0.48 0.23 0.34 0.20 0.30 0.32 0.34 0.27 0.22 0.28 0.35 0.39

VAVGPM 0.44 0.25 0.35 0.23 0.29 0.31 0.33 0.29 0.21 0.27 0.35 0.38

WSAVG -0.26 -0.33 -0.45 -0.37 -0.22 -0.35 -0.33 -0.46 -0.19 -0.29 -0.42 -0.39

WSAVGAM -0.30 -0.38 -0.48 -0.40 -0.25 -0.38 -0.37 -0.49 -0.24 -0.32 -0.46 -0.43

WSAVGPM -0.02 -0.11 -0.17 -0.17 -0.03 -0.11 -0.11 -0.18 -0.01 -0.07 -0.14 -0.11

yr -0.04 -0.27 -0.28 -0.27 -0.02 -0.10 -0.14 -0.27 -0.04 -0.10 -0.35 -0.35
138

Table 10.5 Correlations for morning hydrocarbons species with METDAT variables at Deer Park (Houston area) 

482011039 

1_Butene Benzene Ethyl benzene Ethylene Isobutane Isopentan Propylene Toluene Butane Pentane o-Xylene mp_xylen 

CCAVGPM -0.22 -0.21 -0.24 -0.18 -0.19 -0.29 -0.13 -0.24 -0.29 -0.28 -0.23 -0.24

CCAVTAM -0.29 -0.31 -0.36 -0.30 -0.30 -0.40 -0.20 -0.36 -0.37 -0.38 -0.36 -0.37

MRMAX -0.21 -0.25 -0.10 -0.16 -0.28 -0.20 -0.19 -0.11 -0.42 -0.26 -0.09 -0.08

RAINHRS -0.07 -0.09 -0.16 -0.05 -0.07 -0.16 0.00 -0.13 -0.12 -0.15 -0.15 -0.15

RHAVGMID -0.25 -0.24 -0.28 -0.17 -0.21 -0.33 -0.15 -0.27 -0.29 -0.32 -0.25 -0.27

D24T850 -0.09 -0.01 0.04 -0.01 -0.05 -0.03 -0.07 0.03 -0.04 -0.06 0.06 0.04

D24TD850 -0.08 -0.06 -0.06 -0.07 -0.06 -0.08 -0.06 -0.05 -0.08 -0.09 -0.05 -0.06

DEVH500 -0.16 -0.09 -0.07 -0.10 -0.10 -0.13 -0.08 -0.09 -0.15 -0.13 -0.08 -0.09

DEVH700 -0.11 -0.02 -0.01 -0.03 -0.03 -0.06 -0.01 -0.01 -0.09 -0.06 -0.01 -0.02

DEVT700 -0.21 -0.20 -0.16 -0.20 -0.22 -0.20 -0.21 -0.19 -0.21 -0.21 -0.17 -0.18

DEVT850 -0.18 -0.19 -0.15 -0.20 -0.21 -0.19 -0.19 -0.17 -0.20 -0.20 -0.15 -0.16

DPAVG -0.20 -0.23 -0.08 -0.14 -0.25 -0.20 -0.16 -0.09 -0.40 -0.25 -0.07 -0.07

DPMAX -0.20 -0.22 -0.08 -0.14 -0.25 -0.19 -0.17 -0.09 -0.38 -0.24 -0.07 -0.07

DT700 0.09 0.19 0.11 0.12 0.18 0.15 0.07 0.10 0.31 0.16 0.11 0.09

DT850 0.09 0.19 0.14 0.12 0.16 0.15 0.07 0.13 0.27 0.17 0.14 0.13

DT925 0.16 0.27 0.28 0.21 0.24 0.27 0.14 0.27 0.32 0.26 0.29 0.28

H700 -0.10 -0.06 0.04 -0.03 -0.09 -0.05 -0.02 0.04 -0.25 -0.08 0.04 0.05

MHMAX -0.02 0.02 0.11 0.06 0.01 0.06 0.00 0.10 -0.06 0.02 0.10 0.11

SLPAVG 0.16 0.22 0.14 0.16 0.24 0.20 0.19 0.15 0.29 0.23 0.12 0.13

STPAVG 0.16 0.22 0.13 0.16 0.24 0.20 0.19 0.15 0.28 0.23 0.12 0.13

T700 -0.18 -0.20 -0.09 -0.15 -0.23 -0.17 -0.16 -0.10 -0.35 -0.20 -0.10 -0.09

T850 -0.16 -0.20 -0.07 -0.15 -0.24 -0.16 -0.15 -0.08 -0.37 -0.19 -0.07 -0.06

T925 -0.12 -0.15 -0.01 -0.10 -0.18 -0.10 -0.10 -0.02 -0.31 -0.14 -0.01 0.00

TAAVG -0.16 -0.20 -0.04 -0.12 -0.23 -0.15 -0.14 -0.05 -0.38 -0.19 -0.04 -0.02

TAMAX -0.12 -0.15 0.03 -0.07 -0.18 -0.08 -0.11 0.02 -0.31 -0.13 0.04 0.05

TAMIN -0.18 -0.23 -0.10 -0.15 -0.26 -0.20 -0.14 -0.11 -0.43 -0.24 -0.10 -0.09

TAVGAM -0.14 -0.17 -0.01 -0.10 -0.20 -0.11 -0.12 -0.02 -0.35 -0.16 -0.01 0.01

TAVGPM -0.06 -0.08 0.10 -0.02 -0.13 -0.01 -0.07 0.08 -0.24 -0.06 0.10 0.11

TD850 -0.14 -0.18 -0.14 -0.17 -0.20 -0.18 -0.11 -0.13 -0.32 -0.20 -0.14 -0.13

TDELTA 0.24 0.31 0.42 0.26 0.27 0.41 0.13 0.40 0.40 0.37 0.42 0.42

TMAX -0.08 -0.10 0.08 -0.04 -0.14 -0.03 -0.09 0.06 -0.27 -0.08 0.08 0.09

TRANDIS -0.38 -0.45 -0.57 -0.42 -0.42 -0.53 -0.32 -0.57 -0.42 -0.50 -0.56 -0.57

TRANU 0.25 0.21 0.29 0.15 0.15 0.33 0.07 0.28 0.31 0.31 0.27 0.28

TRANV -0.39 -0.39 -0.23 -0.31 -0.43 -0.39 -0.36 -0.27 -0.48 -0.45 -0.20 -0.22

TRANW 0.22 0.15 0.07 0.08 0.17 0.21 0.10 0.07 0.32 0.23 0.05 0.06

WNDRUN -0.38 -0.46 -0.57 -0.43 -0.43 -0.53 -0.34 -0.58 -0.42 -0.51 -0.56 -0.57

UAVG 0.05 -0.04 0.09 -0.11 -0.12 0.10 -0.16 0.05 0.05 0.05 0.08 0.08

UAVGAM -0.04 -0.14 0.05 -0.19 -0.23 0.01 -0.26 0.00 -0.07 -0.05 0.04 0.04

UAVGPM 0.01 -0.07 -0.01 -0.14 -0.12 0.01 -0.15 -0.04 0.03 -0.01 -0.02 -0.02

VAVG -0.33 -0.32 -0.13 -0.23 -0.34 -0.27 -0.32 -0.17 -0.36 -0.35 -0.10 -0.13

VAVGAM -0.40 -0.39 -0.22 -0.30 -0.40 -0.36 -0.38 -0.26 -0.43 -0.43 -0.19 -0.21

VAVGPM -0.28 -0.25 -0.10 -0.17 -0.27 -0.23 -0.24 -0.13 -0.31 -0.29 -0.07 -0.10

WSAVG -0.28 -0.33 -0.50 -0.34 -0.30 -0.43 -0.25 -0.50 -0.27 -0.37 -0.51 -0.51

WSAVGAM -0.27 -0.33 -0.50 -0.34 -0.28 -0.41 -0.24 -0.50 -0.28 -0.36 -0.51 -0.51

WSAVGPM -0.23 -0.26 -0.32 -0.27 -0.22 -0.29 -0.23 -0.34 -0.19 -0.27 -0.31 -0.33

yr -0.07 -0.12 -0.15 -0.16 -0.04 -0.05 -0.09 -0.15 -0.04 -0.05 -0.18 -0.15
Regressions have been run on a select set of meteorological variables that tended to have the 

most significant correlations for all four sites. Tables 10.6 – 10.8 contain the statistical 

summaries of the runs. The “PVALUE” elements are color codes yellow for p-value less than 

0.05, reflecting statistical significance. The “R2” column shows the r-squared values, which 

tend to be fairly low despite the significance. Only the Clinton Dr. site was used for Houston. 

Dtxxx is the highest available pressure level – 850 mb for El Paso and 925 mbfor the other cities. 

Table 10.6 Alkane model 

CBSA type Intercept v am wsa 

am 

trnsdis tdelta dtxxx cloud 

am 

rhmid R2 

DFW PARMS 42.92 -0.34 -1.84 -0.03 1.29 1.70 0.46 0.00 0.30

DFW T 6.16 -1.56 -3.85 -7.05 6.11 9.79 1.95 -0.05 . 

DFW PVALUE 0.00 0.12 0.00 0.00 0.00 0.00 0.05 0.96 . 

DFW L95B 29.25 -0.78 -2.79 -0.03 0.87 1.36 0.00 -0.14 . 

DFW U95B 56.59 0.09 -0.90 -0.02 1.70 2.04 0.92 0.13 . 

ElPaso PARMS 4.97 4.98 -0.12 -0.05 3.19 3.27 1.54 0.45 0.25

ElPaso T 0.32 8.19 -0.13 -4.87 7.80 9.73 2.81 3.13 . 

ElPaso PVALUE 0.75 0.00 0.90 0.00 0.00 0.00 0.01 0.00 . 

ElPaso L95B -25.43 3.78 -1.99 -0.07 2.39 2.61 0.46 0.17 . 

ElPaso U95B 35.37 6.17 1.75 -0.03 3.99 3.93 2.61 0.74 . 

HGB PARMS 99.90 7.82 -3.56 0.02 0.16 5.44 -5.91 1.52 0.06

HGB T 2.08 5.41 -1.24 1.03 0.12 4.85 -3.58 3.66 . 

HGB PVALUE 0.04 0.00 0.22 0.30 0.90 0.00 0.00 0.00 . 

HGB L95B 5.74 4.98 -9.20 -0.02 -2.32 3.24 -9.15 0.71 . 

HGB U95B 194.07 10.65 2.08 0.06 2.63 7.63 -2.67 2.34 . 

Table 10.7 BTEX Model 

CBSA type Intercept v 

am 

wsa 

am 

trnsdis tdeltadtxxx cloud 

am 

rhmid R2 

DFW PARMS 23.60 0.28 -1.41 -0.02 0.61 0.82 0.20 0.02 0.25

DFW T 5.39 2.01 -4.68 -7.90 4.63 7.52 1.32 0.38 . 

DFW PVALUE 0.00 0.05 0.00 0.00 0.00 0.00 0.19 0.70 . 

DFW L95B 15.01 0.01 -2.01 -0.02 0.35 0.61 -0.09 -0.07 . 

DFW U95B 32.18 0.55 -0.82 -0.01 0.87 1.03 0.49 0.10 . 

ElPaso PARMS 12.09 2.23 -0.31 -0.02 1.38 1.72 0.41 0.20 0.28

ElPaso T 1.76 8.28 -0.74 -4.80 7.63 11.58 1.68 3.18 . 

ElPaso PVALUE 0.08 0.00 0.46 0.00 0.00 0.00 0.09 0.00 . 

ElPaso L95B -1.38 1.70 -1.14 -0.03 1.03 1.43 -0.07 0.08 . 

ElPaso U95B 25.56 2.76 0.52 -0.01 1.74 2.02 0.88 0.33 . 

HGB PARMS 20.20 1.00 -1.90 -0.01 0.75 1.09 -1.26 0.36 0.19

HGB T 2.76 4.55 -4.21 -3.95 3.91 6.42 -5.02 5.61 . 

HGB PVALUE 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 . 

HGB L95B 5.83 0.57 -2.78 -0.02 0.37 0.76 -1.76 0.23 . 

HGB U95B 34.57 1.43 -1.01 -0.01 1.13 1.43 -0.77 0.48 . 

139
Table 10.8 HRVOC Model 

CBSA type Intercept v am wsa 

am 

trnsdis tdeltadtxxx cloud 

am 

rhmid R2 

DFW PARMS 7.71 0.05 -0.47 -0.01 0.33 0.34 0.08 0.02 0.25

DFW T 3.63 0.81 -3.28 -5.05 5.11 6.45 1.17 1.08 . 

DFW PVALUE 0.00 0.42 0.00 0.00 0.00 0.00 0.24 0.28 . 

DFW L95B 3.54 -0.08 -0.76 -0.01 0.20 0.24 -0.06 -0.02 . 

DFW U95B 11.89 0.18 -0.19 0.00 0.46 0.44 0.22 0.06 . 

ElPaso PARMS 8.12 0.91 -0.17 -0.01 0.45 0.77 0.07 0.11 0.24

ElPaso T 2.71 7.81 -0.91 -3.36 5.62 11.77 0.70 3.74 . 

ElPaso PVALUE 0.01 0.00 0.36 0.00 0.00 0.00 0.48 0.00 . 

ElPaso L95B 2.24 0.68 -0.53 -0.01 0.29 0.64 -0.13 0.05 . 

ElPaso U95B 14.00 1.14 0.19 0.00 0.60 0.89 0.28 0.16 . 

HGB PARMS 2.66 0.92 -0.49 0.00 0.61 1.42 -1.12 0.56 0.11

HGB T 0.29 3.26 -0.86 0.04 2.55 6.65 -3.56 7.03 . 

HGB PVALUE 0.77 0.00 0.39 0.97 0.01 0.00 0.00 0.00 . 

HGB L95B -15.40 0.36 -1.61 -0.01 0.14 1.00 -1.74 0.40 . 

HGB U95B 20.72 1.47 0.63 0.01 1.09 1.84 -0.50 0.71 . 

The next step is to regress the annual mean residuals resulting from the meteorological 

regressions on year. This is similar to the Camalier et al. approach, but omitting the spline fits 

and using two stages of OLS. The need for log-transforms has been reduced since the dependent 

hydrocarbon variables were formed from averaging over four hours and then adding across 

species. The results of the met-adjusted residual regressions appear in Figures 10.49 – 10.57 and 

are summarized in Table 10.9. Note that one observation is excluded from calculations in 

Figures 10.49 – 10.51 for Clinton Drive, as there were too few observation in 1996. 

Table 10.9 Results of regressing annual mean residuals of the regression of composite species in 

meteorological variables 

Site Description Model Intercept slope R2 T p-value 

Clinton Dr Alkanes 18.05 -1.53 0.08 -0.86 0.41

BTEX 22.44 -1.92 0.70 -4.48 0.00

HRVOC 16.35 -1.37 0.77 -5.55 0.00

Dallas Hinton Alkanes 19.26 -1.57 0.41 -2.63 0.03

BTEX 16.27 -1.34 0.61 -3.96 0.00

HRVOC 5.68 -0.49 0.45 -2.88 0.02

El Paso Chamizal Alkanes 25.61 -2.01 0.03 -1.87 0.10

BTEX 27.18 -2.13 0.76 -5.09 0.00

HRVOC 10.17 -0.78 0.47 -2.64 0.03

BTEX and HRVOC species appear to be declining significantly. Alkanes are significant only at 

Dallas Hinton, but do show negative slopes at the other two sites. 

140
Figure 10.49 HGB met-adjusted alkane trend, years since 1990 

141
Figure 10.50 HGB met-adjusted BTEX trend, years since 1990 

142
Figure 10.51 HGB met-adjusted HRVOC trend, years since 1990 

143
Figure 10.52 DFW met-adjusted alkane trend, years since 1990 

Figure 10.53 DFW met-adjusted BTEX trend, years since 1990 

144
145
Figure 10.54 DFW met-adjusted HRVOC trend, years since 1990 

146
Figure 10.55 El Paso met-adjusted alkane trend, years since 1990 

147
Figure 10.56 El Paso met-adjusted BTEX trend, years since 1990 

148
Figure 10.57 El Paso met-adjusted HRVOC trend, years since 1990 

149
11. PM2.5 Trends 

Particulate matter sized 2.5 microns and less (PM2.5) is a complicated pollutant. First, it is not a 

single chemical, and the instruments that measure it are varied. In general, data collected by the 

federal reference method samplers (FRMs) are used to determine NAAQS compliance, based on 

a variable that is reported under parameter code 88101. However, speciation monitors generate 

very similar results, and in Texas the speciation sampler is generally identical to the FRM 

sampler. Speciation PM2.5 mass is reported as parameter number 88502. Recently there has 

been some confusion as to which parameter is reported, and more detail can be provided on this 

matter upon request. The point is that based on the similar behavior of the data, both 88101 and 

88502 have been used herein without distinction. 

The approach taken has been to use the annual mean for individual sampling sites that have at 

least 50 percent data return every quarter for at least six years ending in 2007. In Texas, 22 sites 

meet these criteria giving good coverage for the state: 

• Dallas / Fort Worth (5 sites) 

• Northeast Texas (2 sites) 

• Houston area (4 sites) 

• Beaumont / Port Arthur area (2 sites) 

• Austin (1 site) 

• Corpus Christi (2 sites) 

• Lower Rio Grande Valley (1 site) 

• El Paso (3 sites), 

• Odessa (1 site) 

• Big Bend (1 site) 

The OLS regression results show that the five Dallas / Fort Worth sites and one Northeast Texas 

Texarkana site show statistically significant downward trends. All other sites but one show no 

significant trend. 

Only one site in the state shows a statistically significant upward trend, and that is the Houston 

Clinton Drive site 482011035. This site is a known hotspot affected by dust from local activities 

in the prevailing upwind direction to the south. More recent data from 2008 shows a possible 

reversal in the trend for the past couple of years. 

Results are summarized in Table 11.1 and graphs of the annual time series and OLS regression 

lines appear in Figures 11.1 – 11.22. Note that units on the y-axes are missing and should be 

micrograms per cubic meter. 

150
Table 11.1 Summary of OLS regressions of annual mean PM2.5 on year, years since 2000 

airs Site Description Intercept slope R2 T 

pvalue L95B U95B

480370004 Texarkana 14.49 -0.31 0.54 -2.64 0.04 -0.60 -0.02

480430101 BRAVO Big Bend C67 6.78 -0.16 0.39 -2.13 0.07 -0.34 0.02

481130050 Convention Ctr C312 14.62 -0.35 0.48 -2.53 0.04 -0.68 -0.02

481130069 Dallas Hinton St. C401 13.53 -0.37 0.72 -4.24 0.00 -0.58 -0.17

481130087 Dallas Exec Air C402 12.05 -0.20 0.53 -2.60 0.04 -0.38 -0.01

481350003 Odessa Hays C47 7.15 0.15 0.46 2.07 0.09 -0.04 0.34

481410037 El Paso UTEP C12 9.68 -0.08 0.17 -1.00 0.36 -0.29 0.13

481410044 Chamizal C41 9.30 0.05 0.04 0.55 0.60 -0.17 0.28

481410053 El Paso Sun Metro C40 15.82 0.31 0.18 1.03 0.35 -0.46 1.09

482010024 Houston Aldine C8 12.63 0.11 0.13 0.85 0.43 -0.22 0.43

482010058 Baytown A148 12.81 -0.26 0.38 -1.93 0.10 -0.58 0.07

482011035 Clinton C403 13.79 0.31 0.75 4.24 0.01 0.13 0.48

482011039 Deer Park C35 11.40 0.04 0.01 0.24 0.82 -0.33 0.40

482030002 Karnack C85 11.67 -0.01 0.00 -0.09 0.93 -0.42 0.39

482150043 Mission C43 10.70 0.05 0.04 0.50 0.64 -0.19 0.28

482450021 PA Mem School C303 11.42 0.02 0.01 0.20 0.85 -0.18 0.21

483550032 CC Huisache C98 10.13 0.07 0.06 0.62 0.56 -0.20 0.34

483550034 Dona Park C635 8.50 0.26 0.47 2.11 0.09 -0.06 0.59

483611001 West Orange C9 11.55 -0.06 0.05 -0.51 0.63 -0.34 0.23

484391002 Ft. Worth NW C13 12.48 -0.21 0.66 -3.38 0.01 -0.37 -0.06

484391006 Haws Athletic Ctr C310 13.73 -0.32 0.49 -1.96 0.12 -0.77 0.13

484530020 Audubon C38 9.20 0.03 0.03 0.32 0.77 -0.27 0.33

151
Figure 11.1 PM2.5 Annual mean OLS trend, Austin Audubon C38, years since 2000 

Figure 11.2 PM2.5 Annual mean OLS trend, Fort Worth Haws Athletic Center C310, years since 2000 

152
Figure 11.3 PM2.5 Annual mean OLS trend, Dallas Hinton St. C401, years since 2000 

Figure 11.4 PM2.5 Annual mean OLS trend, Dallas Convention Center C312, years since 2000 

153
Figure 11.5 PM2.5 Annual mean OLS trend, Dallas Executive Airport C402, years since 2000 

Figure 11.6 PM2.5 Annual mean OLS trend, Fort Worth NW C13, years since 2000 

154
Figure 11.7 PM2.5 Annual mean OLS trend, Deer Park C35, years since 2000 

Figure 11.8 PM2.5 Annual mean OLS trend, Aldine C8, years since 2000 

155
Figure 11.9 PM2.5 Annual mean OLS trend, Baytown, years since 2000 

Figure 11.10 PM2.5 Annual mean OLS trend, Clinton Drive C403, years since 2000 

156
Figure 11.11 PM2.5 Annual mean OLS trend, Texarkana, years since 2000 

Figure 11.12 PM2.5 Annual mean OLS trend, Karnack C85, years since 2000 

157
Figure 11.13 PM2.5 Annual mean OLS trend, West Orange C9, years since 2000 

Figure 11.14 PM2.5 Annual mean OLS trend, Port Arthur Memorial School C303, years since 2000 

158
Figure 11.15 PM2.5 Annual mean OLS trend, Mission C43, years since 2000 

Figure 11.16 PM2.5 Annual mean OLS trend, Corpus Christi Dona Park C635, years since 2000 

159
Figure 11.17 PM2.5 Annual mean OLS trend, Corpus Christi Huisache C98, years since 2000 

Figure 11.18 PM2.5 Annual mean OLS trend, El Paso UTEP C12, years since 2000 

160
Figure 11.19 PM2.5 Annual mean OLS trend, Big Bend C67, years since 2000 

Figure 11.20 PM2.5 Annual mean OLS trend, El Paso Chamizal C41, years since 2000 

161
Figure 11.21 PM2.5 Annual mean OLS trend, Odessa Hays C47, years since 2000 

Figure 11.22 PM2.5 Annual mean OLS trend, El Paso Sun Metro C40, years since 2000 

162
163

Summary 

• Ozone coming down in Houston, DFW, inconclusive elsewhere 

• SO2, CO generally coming down everywhere 

• NOx in key industrial areas of Houston coming down 

• VOCs coming down at 3 PAMS Type 2 sites (peak emissions locations) – weak trend at 

Deer Park!