## Final Report, March 16, 2009 Partially Satisfying Task 6.2
PROPOSAL FOR GRANT ACTIVITIES UND ER 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 tr ends in air pollutants in a manner that will allow an assessment of the effectiveness of emission c ontrols. Pollutant concentrations are known to be random variables that are f unctions of weather and emissi ons. Furthermore, “weather” comprises local weather and meso- to synoptic -scale meteorology, which affects pollutant transport, and “emissions” comprises loca l 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 b ack as practical, in all nonattainment and near nonattainment areas in the state of Texas. These nonattainment and n ear 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 Vi ctoria area. These data include one-hour ozone, eight-hour averaged ozone, nitrogen oxide (NO), nitrogen dioxide (NO 2 

### ), oxides of nitrogen (NO X 

### ), nonmethane hydrocarbons (NMHC), car bon monoxide (CO), all air toxi cs data including benzene, all pertinent and available meteorological data including upper level te mperature, sea-level pressure gradients, surface wind speed, surf ace wind direction, upper level wind speed, and upper level wind direction.

The second phase involves data analysis for trends. Two genera l approaches are used. First, using a least squares linear regre ssion with appropriate statistical tests, trends for pollutant data in all nonattainment and near nonattainment areas have been analyzed. Co mplicating this simple approach is the selection of the metrics to trac k. The report includes di scussion of rationale for having selected specific time periods during the ozone season and hours of the day. Second, using more advanced methods, trends have be en analyzed taking meteorology into account. For ozone, a generalized linear model (GLM) has been used to remove meteorological influences from the ozone trends. Less sophi sticated efforts have been em ployed 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 soft ware, although R, SPSS, and MS Excel have also been used to a sma ll 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 O 3 

### ordinary least squares trends 6.2 O 3 

### 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 a ssess air-shed delineations 6.2.4 GLM development 6.2.5 Results

7. Oxides of nitrogen (NOx) trends 7.1 NO x 

### ordinary least squares trends

8. Carbon monoxide trends 8.1 CO ordinary least squares trends

9. Sulfur dioxide (SO 2 

### ) trends 9.1 SO 2 

### ordinary least squares trends

10. Hydrocarbon (HC) species trends (highly reactive vol atile organic compounds (HRVOC), benzene, toluene, ethyl benzen e, 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 sh ed 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 countie s have at least 12 years of data ending in 2007. Using the long time series of data, a multivaria te analysis of ozone across Region 6 during summers from 1995 – 2007 reveals six geographical region factors.

An upper air meteorological da taset called METDAT was acquire d. 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 gra duate 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) al lowing more ozone sites representing 71 counties for analysis, a revised factor analysis fo r 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 av erage daily maximum 8-hour ozone averages (oz8hrpk) at the Aldine CAMS 8 s ite versus time shows a statistically significant downward slope in this measure from 1990 thr ough 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 loga rithm of oz8hrpk for 65 sites with 10 years of data with the METDAT variables reveals patterns of correla tion of ozone with meteorological variables related to the seven factors derived in the July report.

An ordinary least squares re gression of late summer (August – September) mean oz8hrpk shows 86 out of 97 sites show negative sl opes, and Harris County sites have the largest d ecline. 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 in formation requested in this project, but with much less detail, at www.epa.gov/airtrends

.
A regional East Texas trend an alysis based on counting monito r-days with oz8hrpk >= 75 parts per billion (ppb) concentrati on 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 qualitativel y 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 ch anges in the number of ozone monitoring sites in metropolitan areas over time, which TCEQ has found to be a signi ficant factor in trends. Using the core based statistical area (CBSA) geographic classificat ions 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 resu lts in ordinary least squares regressions.

Using SAS generalized linear model (GLM) pro cedure to regress ln(o z8hrpk) from Aldine CAMS 8 on METDAT data from Bu sh International Air port shows a statistic ally significant relationship with a “pseudo-R 2 

### ” 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 O 3 

### ordinary least squares trends

• O 3 

### meteorologically-adjusted tr ends (correlation analysis)

• Revised NO, NO 2 

### , NO x 

### ordinary least squares trends

• CO ordinary least squares trends

• SO 2 

### ordinary least squares trends

• Revised hydrocarbon (HC) species trends

• Initial discussion of meteorologically-adj usted trends for hydrocarbons (correlation analysis) Conclusions: Combining data from several urban sites with similar diurnal pattern for oxides of nitrogen (NO
x 

### ), carbon monoxide (CO), and hydrocarbons (H C) 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 su lfur dioxide (SO 2 

### ) 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 nu mber of years since 1990 (x = 0 ... 17).

• All Houston area sites except Galveston CAMS 34 have negative slopes for NO, NO 2 

### and NO x 

### . Of the 24 regressions on NO
x 

### , 17 were significant at p=0.05. Results from nonHouston area sites were gra phed, but were not summarized fo r statistical si gnificance 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 leve l, but it is close (p=0.06).

• For SO 2 

### , for 16 sites across the state, two variab les 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 ha ve 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 posse ssion. 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 co ntaining SAS data sets of aggregated national meteorological data from 1995 – 2007 from the Omnibus Meteorological Database 1 

### (METDAT). These data sets contain surface and upper ai r data, including summarized 24-hour HYSPLIT 2 

### back-trajectories.

The METDAT data come in files for individual cal endar 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. SA S code is provided in Appendix 2 and as a separate electronic attachment (metdat.sas).

The METDAT SAS code sets up the data in thre e 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 fo r 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 mete orological variables and dropping some unused date variables in the 1995-2004 data files.

The data set provided by Mr. Gri bbin also includes loca tion 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 datase t containing ambient pollu tion data based on date and proximity (using a nearest ne ighbor 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 In tegrated 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 App lications and Display sYstem (READY) Website (http://www.arl.noaa.gov/ready/hysp lit4.html). NOAA Air Resources La boratory, Silver Spring, MD.Table 4.1 Types of M ETDAT 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 l east squares regression trends were run both on daily data, which have auto-correlation issues that degrade the quality of statistic al tests, and on annual means.

For NO x 

### , CO, and hydrocarbons, the median diurnal patterns of the species data were examined to select a three or four hour period in the morn ing with the highest medi an concentrations. For NO x 

### , trends were run for nitric oxi de (NO), nitrogen dioxide (NO 2 

### ), and total oxides of nitrogen (NO x 

### ). 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-butan e, isobutene, n-pentane, iso-pentane). For Sulfur dioxide (SO 2 

### ), an examination of diurnal pattern s across the state failed to reveal a common diurnal pattern as was the case with other pollutants. SO 2 

### is principally a pollutant associated with coal burning, and in industria l areas to fugitive emissions from the sulfur removal process from feed stocks. Because of its lack of regular diurnal pattern, SO 2 

### 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 Oct ober 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 examinati on shows the clusters are very similar to those run for two previous projects, cen tered 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 ur ban areas and their use in assessing ozone trends. Atmospheric Environment 41 , 7127-7137.



### 7

### 8 separately assessed as one form of meteorolog ically 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 comparis ons between trends based on the 30-day moving average of daily ozone eight-hour maxima by CBS A using only the subset of monitors with nearly-continuous data during th e period of study. The moving average time series are not appropriate for ordinary least squa res (OLS) regression owing to th e 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 th e purpose of showing simple quali tative 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 r ough 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 appro ach 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 Prefe rred Indicator over Exceedanc e Day Counts,” TCEQ Trade Fair, 2004.



### 9Figure 6.2 BPA 8-hr max 30-day mo ving 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

10Figure 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 movi ng 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

11Figure 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 re gression line using only long-running sites’ data



### Sites used: 483550025, 483550026; p < 0.0001

12Figure 6.8 CC 8-hr max 30-day moving average with re gression line using only long-running sites’ data



### Sites used 481810001; p = 0.0004

131. SAS code to determine long-running ozone sites and extendtime series by merging nearby “early” and “later” sites(filename getozone longterm data.sas:libnamecams2"D:\cams2";**run**;libnameother"C:\Documents and Settings\Sully\My Documents\mother\OTHERDATA";**run**;* This program prepares data for merger with the meteorological data sets provided by Erik Gribbin;* Read in all the data from 1990-2007 from MOTHER;* An earlier assessment (shown at bottom of program and using Excel) suggested thattime series for some monitors could be concatenated because of close proximityand one having ended shortly before the other began (i.e., the site moved).;**data**ozone_sites0;setcams2.oz_1990 cams2.oz_1991 cams2.oz_1992 cams2.oz_1993 cams2.oz_1994cams2.oz_1995 cams2.oz_1996 cams2.oz_1997 cams2.oz_1998 cams2.oz_1999cams2.oz_2000 cams2.oz_2001 cams2.oz_2002 cams2.oz_2003 cams2.oz_2004cams2.oz_2005z cams2.oz_2006 cams2.oz_2007;ifairs ="481210033"thenairs ="481210034";ifairs ="481130045"thenairs ="481130075";ifairs ="483390089"thenairs ="483390078";ifairs ="481410028"thenairs ="481410055";ifairs ="401430127"thenairs ="401431127";ifairs ="482011003"thenairs ="482011039";ifairs ="481670014"thenairs ="481671034";ifairs ="051191005"thenairs ="051191008";ifairs ="482011037"thenairs ="482010075";ifairs ="350431002"thenairs ="350431003";ifairs ="482011041"thenairs ="482011015";ifairs ="484230004"thenairs ="484230007";ifairs ="220470002"thenairs ="220470012";ifairs ="350011003"thenairs ="350010029";ifairs ="220730002"thenairs ="220730004";ifairs ="350131012"thenairs ="350130023";ifairs ="482450102"thenairs ="482451035";ifairs ="350010028"thenairs ="350010024";ifairs ="220570002"thenairs ="220570004";ifairs ="484390057"thenairs ="484393011";ifairs ="220870002"thenairs ="220870009";ifairs ="480391003"thenairs ="480391016";ifairs ="400870073"thenairs ="400871073";14ifairs ="400310647"thenairs ="400310649";ifairs ="480290036"thenairs ="480290052";/* if airs in ("481210034" "481130075" "483390078" "481410055" "401431127" "482011039" "481671034"*//* "051191008" "482010075" "350431003" "482011015" "484230007" "220470012" "350010029"*//* "220730004" "350130023" "482451035" "350010024" "220570004" "484393011" "220870009"*//* "480391016" "400871073" "400310649" "480290052") ;*/keepairs date oz8hrpk oz1hrpk oz1hrav oz1hrvh oz1hrvd oz8hrvd;**run**;* Figure out the length of the time series for each site;**proc sort** data=ozone_sites0;byairs date;**run**;**data**ozone_sites;setozone_sites0;byairs date;iffirst.airs or last.airs;**run**;**proc sort** data=ozone_sites;byairs date;**run**;**proc transpose** data=ozone_sitesout=ozone_sites;byairs;vardate;**run**;**data**ozone_sites;setozone_sites;early = col1; late = col2;dropcol1 col2;formatearly latemmddyy8.;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_o3_sites.xls"out=nine_sitesdbms=XLS;**run**;**data**site_info;setother.siteinfo_newx2 nine_sites;ifsite_description =""thendo;ifcity ^=""thensite_description = substr(city,**1**,**8**)||" "||substr(county,**1**,**8**)||" "||substr(site_address,**1**,**8**);elsesite_description = substr(county,**1**,**8**)||" "||substr(site_address,**1**,**8**);end;**run**;**proc sort** data=site_info;byairs;**run**;**data**ozone_sites;mergeozone_sites (in=in_o3) site_info;byairs;ifin_o3;keepairs early late duration latitude longitude;**run**;15/*proc export data=ozone_sites outfile="C:\Documents and Settings\Sully\Desktop\190_o3_sites.xls" dbms=XLSreplace; run;*/* which sites have > 15 years of data?;**data**ozone_sites_long;setozone_sites;ifduration >=**15**;**run**;*******************************************************************************2. Compute moving averages for daily maximaozone by urban area (CBSA) (filenamesites over time2.sas)libnameother"C:\Documents and Settings\Sully\My Documents\mother\OTHERDATA";libnamecams2"D:\cams2";**data**oz;setcams2.oz_1990 cams2.oz_1991 cams2.oz_1992 cams2.oz_1993 cams2.oz_1994cams2.oz_1995 cams2.oz_1996 cams2.oz_1997 cams2.oz_1998 cams2.oz_1999cams2.oz_2000 cams2.oz_2001 cams2.oz_2002 cams2.oz_2003 cams2.oz_2004cams2.oz_2005z cams2.oz_2006 cams2.oz_2007;keepairs date oz8hrpk;**run**;**proc sort** data=oz;byairs;**run**;**proc sort** data=ozone_sites_long;byairs;**run**;**data**oz;mergeoz ozone_sites_long (in=inlong);byairs;ifinlong;**run**;**data**site_cbsa;setother.Siteinfo_newx2;keepairs cbsa_code cbsa_title state;**run**;**proc sort** data=site_cbsa;byairs;**run**;**data**oz_cbsa;16mergesite_cbsa oz (in=inoz);byairs;ifinoz;ifcbsa_code;**run**;**proc sort** data=oz_cbsa;bydate state cbsa_title;**run**;**proc means** data=oz_cbsanoprint;bydate state cbsa_title;outputout=oz_cbsa_statsmax(oz8hrpk)=oz8hrmaxn(oz8hrpk)=num_sites;**run**;**data**tx_oz_cbsa_stats;setoz_cbsa_stats;ifstate ="TX";dropstate _TYPE_ _FREQ_;ifsubstr(cbsa_title,**1**,**2**) in ("Au""Be""Da""El""Ho""Sa" “Co” “Lo”);**run**;**proc sort** data=tx_oz_cbsa_stats;bydate;**run**;**data**days;formatdatemmddyy8.;formatcbsa_title$50.;date = mdy(**1**,**1**,**1990**);dowhile(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;bydate cbsa_title;**run**;17**data**tx_oz_cbsa_days;mergedays tx_oz_cbsa_stats;bydate cbsa_title;ifnum_sites =**.**thennum_sites =**0**;**run**;* moving averages ;**proc sort** data=tx_oz_cbsa_days;bycbsa_title date;**run**;**data**tx_oz_cbsa_days2;settx_oz_cbsa_days;formatlast_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);1819oz8_lag_1 = lag(oz8hrmax);oz8_lag_0 = oz8hrmax;if( last_cbsa = cbsa_title )thendo;mov_avg30 = mean( of oz8_lag_0-oz8_lag_29 );num_days = n( of oz8_lag_0-oz8_lag_29 );ifnum_days <=**20**thenmov_avg30 =**.**;end;elsedo;mov_avg30 =**.**; num_days =**.**;end;**run**;3. Open SAS InteractiveAnalysis, select thetx_oz_cbsa_days2dataset and fit variablesmov_avg30vsdatebycbsa_title.

## 6.2 Meteorologically-adjus ted Trends for Ozone

6.2.1 Houston-Galveston-Brazor ia Conceptual Model Input

In comparing the surface and upper air meteor ological 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 pollu tion 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 di stribution 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 Moni toring 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 Brazori a County were connected w ith the time series of data at the currently active Lake Jackson site because of the close prox imity between sites and the fact that an examinati on 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.

20Figure 6.9 Count of days for which ea ch site presented the area maximu m 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 freque ncy tend to be in downwind suburban or rural

areas.

As was mentioned earlier, the transformed ozone variable employed in subse quent 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 ye ars 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 th e corresponding day’s METDAT da ta 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 Austi n, which is just under 50 miles from the upper air site at Fort Hood. This may in part explain why Austin correla tions 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 th e variables used in the Camalier, Cox, and Dolwick analysis 5 

### . 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 ur ban areas and their use in assessing ozone trends. Atmospheric Environment 41 , 7127-7137.



### 21

### 22 behavior of the correlations w ithin this table. Thus, South Texas areas are grouped, Central and Upper Gulf Coast areas are grouped, and Dallas/Fo rt Worth and El Paso stand alone. It should be noted that many lower correlations not highlig hted may be statistically significant because of the large number of av ailable observations.

Examples of the behavior of the relationships fo r three of the highest ab solute 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 re lationship between stagnation and pollutant accumulation. Figure 6.11 shows the important fact that the drier an ai r mass, the higher the ozone concentration. Caution must be used in assessing this relations hip, 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 Gu lf. Figure 6.12 shows that the difference between the maximum a nd 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 pa ge). 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 pars imonious model with only the best set of 10 largely independent variables reduces the R 2 

### to 47 percent. Implementing a more complicated parsimonious general linear model (GLM) with spline functions to reduce short-term random variations and to accommodate nonlin ear relationships and including a circular spline function for the TRANDIR (net direction of HYSPLIT back-t rajectory) variable plus including a set of dummy variables for “ year ”, the R 2 

### improves to 67 percent. Th is GLM regression approach shows no day-of-the week effect, but does s how a statistically significant decline in meteorologically-adjusted ozone levels from 1995 – 2007. More details on the GLM regression approach are provided in secti ons 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. LS T) average temp erature 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) wi nd 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) wi nd 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 humid ity (%) % 24-hr calc RHAVG 18 Midday (10 a.m.-4 p.m. LST) average re lative 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 temperat ure 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 temper ature 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 cl oud 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) 4a m-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 Trans port 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) Temperat ure at 850 mb from 10-yr monthly mean C 1-hr calc DEVT850 56 Deviation in Morning (~1200 UTC) Temperat ure at 700 mb from 10-yr monthly mean C 1-hr calc DEVT700 57 Deviation in Morning (~1200 UTC) Temperat ure 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 0.29Figure 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



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



### 26

## 6.2.2. Assessing Transport Affectin g the Houston Region Using HYSPLIT Back-trajectories Alone

The National Oceanic and Atmospheric Admi nistration (NOAA) Air Resource Laboratory (ARL) maintains software, data files, and documen tation useful in assessing pollution transport. The HYSPLIT 4.8 6 

### desktop computer application was used with Eta Data Assimilation System (EDAS) datasets to assess the year to year vari ability 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 Hous ton, 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 Figu re 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. Upwi nd 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 contro l region (AQCR) – and assuming th ese 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 suggesti ng that the pollution has been transported into the area from a location relativ ely near the modeled centerline. Furthermore, when many trajectories are used and a correlation is establishe d 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 highe r than average concentrations, and some other areas/paths are associated w ith cleaner air and lower concentrations.

6 Draxler, R.R. and Rolph, G.D., 2003. HYSPLIT (HYbrid Single-Particle Lagrangian In tegrated 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 App lications and Display sYstem (READY) Website (http://www.arl.noaa.gov/ready/hysp lit4.html). NOAA Air Resources La boratory, Silver Spring, MD.

.



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



### 28Figure 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 HYPSL IT was used to sequester individual back-trajectories into a relatively small se t of classes based on shape and direction. (Details on the clustering algorithm originally composed for an ear lier contract report appears below.) The mean centerlines for th e 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 a ny cluster, and have been assigned cluster 0. All but one of these cluster 0 trajectorie s run to the south and would go beyond the modeling domain for the EDAS data, so th ey do represent a mean ingful cluster to themselves with one outlier. Figure 6.23 s hows a histogram summarizing the number of trajectories per cluster.

• In a separate process, the ozone data were an alyzed 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, th e 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 identifyi ng 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_ma x” and “background” and the daily difference between them – “difference” – were calcu lated 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 re sults 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



### 30Figure 6.16 Cluster 0 72-hour ba ck-trajectories from Houston

Figure 6.17 Cluster 1 72-hour ba ck-trajectories from Houston



### 31Figure 6.18 Cluster 2 72-hour ba ck-trajectories from Houston

Figure 6.19 Cluster 3 72-hour ba ck-trajectories from Houston



### 32Figure 6.20 Cluster 4 72-hour ba ck-trajectories from Houston

Figure 6.21 Cluster 5 72-hour ba ck-trajectories from Houston



### 33Figure 6.22 Cluster 6 72-hour ba ck-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



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



### How Trajectory Clustering Works

(This text first appeared in a report under a nother 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 clusters 7 

### . 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 individu al trajectories or clus ters based on minimizing each step’s effect on increasing the total spatial variance (TSV) with in all clusters. In the early steps of clustering, the TSV may increase as each ne w 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 sm all and steady. However, eventually there is a

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



### 35

### relatively large jump in TSV, as two dissimilar cl usters are joined. Thus , the number of clusters just before the jump is a good choice for total numb er of factors. In Figure 6.25, a jump occurs from six to five clusters, with another sharp jump from four to three cluste rs. 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 summ ary 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 cl uster, 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 ta ble 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 monitori ng sites used in the assessment to maximize the inclusion of clear upwind and dow nwind monitoring sites. The Harris NW CAMS 26, the Lake Jackson / Clute combined site pa ir, and the Galveston Airport / 99 th 

### 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 makesclear 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 trajec tory cluster is a fact or 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 statistica l significance (p=0.03). This second graph on difference may suggest the local contribution may also be relate d to back trajectories. An explanation for this is that the slower air m ovement of the “short” cluster 4 allows greater accumulation in the area under lower local surfac e 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) repor t to lead to very high ozone readings.

The bottom line is that varia tion in synoptic scale back trajectories can account for around 24 ppb of variation in background concentrati ons and around 33 ppb in area max on average (comparing Cluster 1 and Cluster 5 results).

37Figure 6.26 Relationship between mean max and mea n min (background) by cluster, and relationship between “local contribution” (d ifference max – min) by cluster

y = 1.36x + 12.76

R 2 = 0.96

y = 0.36x + 12.67

R 2 = 0.65

15 25 35 45 55 65 75 85

15 20 25 30 35 40 45 50 Area wide minimum (background) ppbArea 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 A ssess Air-shed Delineations

In an early report on this project, a factor analysis using histor ical ozone data (1995-2007) was used to delineate geographic areas with distin ct 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 availa ble 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 m odel, 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 sugge sts 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 “q uartimin” rotation. A table of the rotated factor loadings with geographic labels appears in Tabl e 6.6. The table is organized to group counties with similar loadings on a given factor. Ce lls are color-coded by loading – yellow for 0.30-0.50, orange for 0.50-0.70, and red for gr eater 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 coun ties listed above with th e clusters of factors found appears in Figure 6.27. Oval s are drawn to repres ent the large uncertain ty in actual air shed boundaries and the considerab le overlap across factors for many counties as seen in the factor loadings in Table 6.6.

39Table 6.6 Rotated factor loadings for EPA R6 counties



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



### 41

## 6.2.4 GLM Development The first step in the development of the mode l following the EPA Camalie r et al. method is to examine the relationship between the chosen ozon e 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”, a nd “tdelta”. In each of these, a linear fit was applied. A “linear” fit is one in stance of a model that is a sm ooth 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 conjoini ng functions are equal. One exam ple of this approach appears in Figure 6.28. In selecting a sp line model, a user must consid er trade-offs between smoothness and the model fit. The number and location of “knots” that are the endpo ints of the connected model segments are user contro lled. SAS spline calculations ar e predicated on balancing the improved fit against the increased roughness in the curve using a penalty function incorporated into the ordinary least sq uares objective function.

Figure 6.28 Generic example of a spline model fit to data



### The approach we follow in this project is a gene ral 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 PR OC 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 approximate ly 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

R 2 

### = 0.99

58 59 60 61 62 63 64 65

56 58 60 62 64 66
CCD R-code Cincinnati met-explained O3 ppbProc Transreg Cincinnatimet-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 TRAN SREG, GLM, GAM (experimental), and GLIMIX (experimental). Accuracy of re sults 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 relati vely 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 meteorol ogy, concentrations of ozone h ave declined significantly in the Dallas-Fort Worth and Houston areas. A ll 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.

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

Figure 6.31 Austin-Round Rock residua l meteorologically-adjusted trend model



### 44Figure 6.32 Beaumont-Port Arthur actual annual summer-mean and met eorologically-adjusted time series

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



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

Figure 6.35 Corpus Christi residual meteorologically-adjusted trend model



### 46Figure 6.36 Dallas-Fort Worth ac tual annual summer-mean and meteor ologically-adjusted time series

Figure 6.37 Dallas-Fort Wo rth residual meteorologically-adjusted trend model



### 47Figure 6.38 El Paso actual annual summer-mean and meteorol ogically-adjusted time series

Figure 6.39 El Paso residual met eorologically-adjusted trend model



### 48Figure 6.40 Houston-area actual annual summer-mean and meteoro logically-adjusted time series

Figure 6.41 Houston-area residual me teorologically-adjusted trend model



### 49Figure 6.42 San Antonio actual annual summer-me an and meteorologically-adjusted time series

Figure 6.43 San Antonio residual meteorologically-adjusted trend model



### 50Figure 6.44 Victoria actual annual summer-mean and meteorol ogically-adjusted time series

Figure 6.45 Victoria residual meteo rologically-adjusted trend model



### 51Table 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 st ations in these cities as predictor variables in the model, along with the same meteorological variables as in the pr evious analysis. The following figures show the results in a format simila r 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.

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

Figure 6.47 Dallas residual meteorologically and precursor adjusted trend model



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

Figure 6.49 El Paso residu al meteorologically and precu rsor adjusted trend model



### 54Figure 6.50 Houston actual annual summer-mean and met eorologically and precursor predicted time series

Figure 6.51 Houston residual meteorologica lly and precursor adjusted trend model

Table 6.8 Second round of regressions with NOx and hydr ocarbons 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 ar eas 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 genera lly modeled as 90 percent of fresh NO
x 

### emissions by mass. NO 2 

### is clearly bi-modal with nearly equal p eaks in morning and nighttime values. The resulting NO
x 

### , ideally the sum of NO and NO 2 

### , 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 orde r to improve the numerical accura cy of the regressions and to

56

### make the trends more easily interpretable, a “y ear index” variable was created by subtracting 1990 from each year. We regressed the annual m ean morning concentrations on the year index and produced tabular and graphical results. The calcu lated y-intercept is dire ctly 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 a ppear 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, SA S GPLOT does not allow one to reposition or reformat the regression equation as does MS Excel.

The OLS trends for NO
x 

### 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, NO 2 

### , and NO
x 

### , but the concentrations are extremely low, as eviden ced by the y-intercepts. The urban Bayland Park CAMS 53 site shows a weak not-si gnificant 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 dow nward trends for total NO
x 

### , five show statistically significant downward trends for NO, and five show sta tistically significant downward trends for NO 2



### .

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% 2.0 6 0.07 -0.04 0.75 Galveston Aprt C34 no 0.34 0.06 8 8% 0.8 5 0.42 -0.10 0.21 Galveston Aprt C34 no2 2.70 0.20 8 24% 1.6 0 0.15 -0.09 0.49 Aldine C8 nox 37.82 -0.53 16 25% -2.3 0 0.04 -1.01 -0.04 Aldine C8 no 22.93 -0.51 16 41% -3.3 0 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% -2.7 2 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% -5.9 0 0.00 -0.43 -0.19 Lang C408 nox 70.01 -1.75 16 67% -5.6 7 0.00 -2.40 -1.09 Lang C408 no 48.54 -1.54 16 73% -6.6 3 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% -0.8 9 0.40 -1.93 0.86 Bayland Park C53 no 19.12 -0.47 8 16% -1.2 3 0.25 -1.33 0.40 Bayland Park C53 no2 17.27 -0.12 8 3% -0.4 9 0.64 -0.68 0.44 East C1 nox 58.56 -1.30 13 69% -5.3 7 0.00 -1.82 -0.77 East C1 no 39.60 -1.28 13 77% -6.5 8 0.00 -1.70 -0.86 East C1 no2 19.20 -0.02 13 0% -0.2 2 0.83 -0.24 0.20 Clinton C403 nox 57.13 -1.04 16 64% -5.3 6 0.00 -1.45 -0.63 Clinton C403 no 31.90 -0.35 16 26% -2.3 8 0.03 -0.66 -0.04 Clinton C403 no2 25.02 -0.62 16 62% -5.0 6 0.00 -0.89 -0.36 Crawford C407 nox 80.00 -2.64 9 69% -4.4 9 0.00 -3.96 -1.31 Crawford C407 no 51.46 -1.87 9 59% -3.6 2 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% -2.4 5 0.04 -1.00 -0.04 Deer Park 2 C35 no 8.22 -0.14 9 17% -1.3 7 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 presen ted 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



### 58Figure 7.2 NO 2 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 NO
x 5-8 CST May-Oct. OLS Trends, years since 1990, Galveston Airport C34



### 59Figure 7.5 NO 2 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 NO
x 5-8 CST May-Oct. OLS Trends, years since 1990, Aldine C8



### 60Figure 7.8 NO 2 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 NO
x 5-8 CST May-Oct. OLS Trends, years since 1990, NW Harris C26



### 61Figure 7.11 NO 2 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 NO
x 5-8 CST May-Oct. OLS Trends, years since 1990, Lang C408



### 62Figure 7.14 NO 2 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 NO
x 5-8 CST May-Oct. OLS Trends, years since 1990, Lang C408



### 63Figure 7.17 NO 2 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 NO
x 5-8 CST May-Oct. OLS Trends, years since 1990, East C1



### 64Figure 7.20 NO 2 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 NO
x 5-8 CST May-Oct. OLS Trends, years since 1990, Clinton C403



### 65Figure 7.23 NO 2 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 NO
x 5-8 CST May-Oct. OLS Trends, years since 1990, Crawford C407



### 66Figure 7.26 NO 2 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 NO
x 5-8 CST May-Oct. OLS Trends, years since 1990, Deer Park C35



### 67Figure 7.29

Figure 7.30

Figure 7.31



### 68Figure 7.32

Figure 7.33

Figure 7.34



### 69Figure 7.35

Figure 7.36

Figure 7.37



### 70Figure 7.38

Figure 7.39

Figure 7.40



### 71Figure 7.41

Figure 7.42

Figure 7.43



### 72Figure 7.44

Figure 7.45

Figure 7.46



### 73Figure 7.47

Figure 7.48

Figure 7.49



### 74Figure 7.50

Figure 7.51

Figure 7.52



### 75Figure 7.53

Figure 7.54

Figure 7.55



### 76Figure 7.56

Figure 7.57

Figure 7.58



### 77Figure 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. Inte restingly, 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 represen t 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 fo llowing are not arranged by regi on and are not labeled the same way the NOx graphs are, these will be clea ned up in the next revisi on of this report. The clear pattern of concentration de creases in all areas at all mon itors shows the clear success of emission controls. Historical analysis of co mparisons of CO and hydr ocarbons 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 concentr ations have declined as well.

The summary of regression results appears in Ta ble 8.1. Only the Deer Park site is not statistically significant at a 0.05 le vel, but it is close (p=0.06).

Table 8.1 OLS regrssion results fo r 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



### 80Figure 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



### 81Figure 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



### 82Figure 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



### 83Figure 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 Tre nds, years since 1990, Brownsville C80



### 84Figure 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



### 85Figure 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



### 86Figure 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 Tre nds, years since 1990, Fort Worth NW C13



### 87Figure 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 Tre nds, years since 1990, Houston Aldine C8



### 88Figure 8.17 El Paso Downtown C6



### 89

## 9. Sulfur dioxide (SO 2 

## ) trends
There are both national and state standards for sulfur dioxide (SO 2 

### ). 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 f acility 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 pollutant s concentrations may be high at night, the mean diurnal patterns vary from site to site and provide little sy stematic clues as to a best time period to select for trend analysis. Thus, the simp le 24-hour average has been chosen.

90

## 9.1 SO 2 

## 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



### 91Figure 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



### 92Figure 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



### 93Figure 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



### 94Figure 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



### 95Figure 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



### 96Figure 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



### 97Figure 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



### 98Figure 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



### 99Figure 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



### 100Figure 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



### 101Figure 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



### 102Figure 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



### 103Figure 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



### 104Figure 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



### 105Figure 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, ye ars since 1990, Houston North Wayside C405



### 106Figure 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 al l 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 l ong-running auto-GCs in Dallas, Houston, and El Paso have been selected for analysis. Th ese species repres ent three classes of hydrocarbons:

• Highly reactive species: et hylene, propylene, 1-butene

• “BTEX” substituted aromatics: benzene, toluene, ethyl benzene, m/p-xylene, and oxylene

• Significant alkanes: n-butane, is obutane, n-pentane, isopentane The OLS trends for these 12 species are summar ized in Table 10.1 and graphed in Figures 10.1 – 10.48.

108Table 10.1 Site Description species Intercept slope DoF R2 T p-value L95B U95B
Hinton St. C401 Ethylene 8.55 -0.3 9 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



### 109Figure 10.1 1-Butene 6-9 CS T 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



### 110Figure 10.3 Ethyl Benzene 6-9 CST May-Oct. OL S Trend, years since 1990, Deer Park C35

Figure 10.4 Ethylene 6-9 CS T May-Oct. OLS Trend, years since 1990, Deer Park C35



### 111Figure 10.5 Isobutane 6-9 CS T May-Oct. OLS Trend, year s since 1990, Deer Park C35

Figure 10.6 Isopentane 6-9 CST May-Oct. OL S Trend, years since 1990, Deer Park C35



### 112Figure 10.7 m/p-Xylene 6-9 CST May-Oct. OL S Trend, years since 1990, Deer Park C35

Figure 10.8 n-Pentane 6-9 CST May-Oct. OL S Trend, years since 1990, Deer Park C35



### 113Figure 10.9 n-Butane 6-9 CS T May-Oct. OLS Trend, year s since 1990, Deer Park C35

Figure 10.10 o-Xylene 6-9 CST May-Oct. OL S Trend, years since 1990, Deer Park C35



### 114Figure 10.11 Propylene 6-9 CST May-Oct. OL S Trend, years since 1990, Deer Park C35

Figure 10.12 Toluene 6-9 CST May-Oct. OL S Trend, years since 1990, Deer Park C35



### 115Figure 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



### 116Figure 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. OL S Trend, years since 1990, Chamizal C41



### 117Figure 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



### 118Figure 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



### 119Figure 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



### 120Figure 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



### 121Figure 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



### 122Figure 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



### 123Figure 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



### 124Figure 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



### 125Figure 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



### 126Figure 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



### 127Figure 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



### 128Figure 10.39 Ethyl Benzene 6-9 CST May-Oct. OL S Trend, years since 1990, Clinton Dr. C403

Figure 10.40 Ethylene 6-9 CST May-Oct. OLS Trend, years since 1990, Clinton Dr. C403



### 129Figure 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



### 130Figure 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



### 131Figure 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



### 132Figure 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 morni ng average for the four PAMS m onitoring sites with long term data. Correlations are color-coded such that corr elations greater than or equal to 0.3 are orange, correlations greater than or equa l 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, sugg esting 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 variab le for the year, and the correlation reflects whether a spec ies 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 variable s in the meteorological adjusted trends.

134

### 135 Table 10.2 Correlations for morning hydrocarbons species with METDAT variables at Hinton Street DFW

4811300691_ButeneBenzeneEthylbenzeneEthyleneIsobutaneIsopentanPropyleneTolueneButanePentaneo-Xylenemp_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 0.0 3 -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

4814100441_ButeneBenzeneEthylbenzeneEthyleneIsobutaneIsopentanPropyleneTolueneButanePentaneo-Xylenmp_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

4820110351_ButeneBenzeneEthylbenzeneEthyleneIsobutaneIsopentanPropyleneTolueneButanePentaneo-Xylenemp_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 hy drocarbons species with METDAT vari ables at Deer Park (Houston area)

4820110391_ButeneBenzeneEthylbenzeneEthyleneIsobutaneIsopentanPropyleneTolueneButanePentaneo-Xylenemp_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 se lect set of meteorological vari ables that tended to have the most significant correlations for all four site s. 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-s quared values, which tend to be fairly low despite the significance. Only the Clinton Dr. site was used for Houston. Dtxxx is the highest available pre ssure 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 -0.1 2 -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 tdelta dtxxx 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 -0.3 1 -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 -1.1 4 -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

.



### 139Table 10.8 HRVOC Model CBSA type Intercept v am wsa

am trnsdis tdelta dtxxx 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 -0.1 7 -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 Ca malier 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 resi dual regressions appear in Figures 10.49 – 10.57 and are summarized in Table 10.9. Note that one ob servation is excluded fr om 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 re siduals 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 declini ng significantly. Alkane s are significant only at Dallas Hinton, but do show negative slopes at the other two sites.

140Figure 10.49 HGB met-adjusted alkane trend, years since 1990



### 141Figure 10.50 HGB met-adjusted BTEX trend, years since 1990



### 142Figure 10.51 HGB met-adjusted HR VOC trend, years since 1990



### 143Figure 10.52 DFW met-adjusted al kane trend, years since 1990

Figure 10.53 DFW met-adjusted BTEX trend, years since 1990



### 144

### 145Figure 10.54 DFW met-adjusted HR VOC trend, years since 1990



### 146Figure 10.55 El Paso met-adjusted alkane trend, years since 1990



### 147Figure 10.56 El Paso met-adjusted BTEX trend, years since 1990



### 148Figure 10.57 El Paso met-adjusted HRVOC trend, years since 1990



### 149

## 11. PM 2.5 

## Trends

Particulate matter sized 2.5 microns and less (PM 2.5 

### ) 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 re ported under parameter code 88101. However, speciation monitors generate very similar results, and in Texas the specia tion sampler is generally identical to the FRM sampler. Speciation PM 2.5 

### mass is reported as parameter number 88502. Recently there has been some confusion as to which parameter is re ported, and more detail can be provided on this matter upon request. The point is that based on the similar behavi or of the data, both 88101 and 88502 have been used herein without distinction.

The approach taken has been to use the annual m ean for individual sampli ng sites that have at least 50 percent data return every quarter for at least six years e nding 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 Da llas / Fort Worth sites and one Northeast Texas Texarkana site show statistically significant dow nward trends. All othe r sites but one show no significant trend.

Only one site in the state shows a statistically significant upward trend, an d that is the Houston Clinton Drive site 482011035. This site is a known hotspot affect ed 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 th e 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.

150Table 11.1 Summary of OLS regressions of annual mean PM 2.5 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



### 151Figure 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



### 152Figure 11.3 PM2.5 Annual mean OLS trend, Dallas Hinton St. C401, years since 2000

Figure 11.4 PM2.5 Annual mean OLS trend, Dalla s Convention Center C312, years since 2000



### 153Figure 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



### 154Figure 11.7 PM2.5 Annual mean OLS tre nd, Deer Park C35, years since 2000

Figure 11.8 PM2.5 Annual mean OLS tr end, Aldine C8, years since 2000



### 155Figure 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



### 156Figure 11.11 PM2.5 Annual mean OLS tr end, Texarkana, years since 2000

Figure 11.12 PM2.5 Annual mean OLS trend, Karnack C85, years since 2000



### 157Figure 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



### 158Figure 11.15 PM2.5 Annual mean OLS trend, Mission C43, years since 2000

Figure 11.16 PM2.5 Annual mean OLS trend, Cor pus Christi Dona Park C635, years since 2000



### 159Figure 11.17 PM2.5 Annual mean OLS trend, Cor pus Christi Huisache C98, years since 2000

Figure 11.18 PM2.5 Annual mean OLS trend, El Paso UTEP C12, years since 2000



### 160Figure 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



### 161Figure 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 (p eak emissions locations) – weak trend at Deer Park!