# Final Rapid Science Synthesis Report: Findings from the Second Texas Air Quality Study (TexAQS II)

A Report to the Texas Commission on Environmental Quality

by the TexAQS II Rapid Science Synthesis Team

Prepared by the Southern Oxidants Study O ffice of the Director at North Carolina State University

by Ellis B. Cowling, Director of SOS Cari Furiness, Research Associate Basil Dimitriades, Adjunct Professor and David Parrish Earth System Research Laboratory, National Oceanic and Atmospheric Administration

In cooperation with Mark Estes of TCEQ and 47 other members of the Rapi d Science Synthesis Team

TCEQ Contract Number 582-4-65614

31 August 2007Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 2 

### Acknowledgments

The Texas Commission on Environmental Quality (TCEQ) and the Rapid Science Synthesis Team (RSST) for the Second Texas Air Qualit y Study (TexAQS II) gratefully acknowledge significant contributions to the success of the TexAQS II field research campaign by the many different organizations listed be low. These contributions include significant financial and inkind organizational support as well as the time and creative energy of individual scientists, engineers, post-doctoral fello ws, and graduate students w ho voluntarily joined in this collaborative enterprise.

These contributions include:

• Defining the objectives of TexAQS II,

• Instrumenting the various air-qua lity measurement platforms,

• Implementing and quality assuring the myriad field measurements during the course of the study,

• Completing the analysis, interpretation, and intercomparison of research results, and

• Formulating carefully crafted statements of research findings to be used for policy purposes by TCEQ and other stakeholders.

Cooperating State Government Organizations
California Air Resources Board Texas Commission on Environmental Quality Chief Engineer’s Office (CEO) Air Quality Division (AQD) Air Modeling and Data Analysis (AMDA) Industrial Emissions Assessment (IEA) Toxicology Office of Compliance and Enforcement Field Operations Division Region 4 – Dallas/Fort Worth Region 12 – Houston Monitoring Operations (MonOps) Ambient Monitoring Air Laboratory and Mobile Monitoring Data Management and Quality Assurance

Cooperating Texas Local Government Organizations Alamo Area Council of Governments (AACOG) Capital Area Council of Governments (CAPCOG) City of Corpus Christi City of Victoria

Cooperating Federal Government Agencies Department of Defense (DOD) Navy Postgraduate School (NPS)Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 3 Department of Energy (DOE) Pacific Northwest Laboratory (PNL) Argonne National Laboratory (ANL) Los Alamos National Laboratory (LNL) Department of Interior (DOI) Minerals Management Service (MMS) Environment Canada (EC) Meteorological Service of Canada (MSC) National Aeronautics and Space Administration Headquarters Science Mission Direct orate and science teams for the Atmospheric Infrared Sounder (AIRS), Cloud-Aerosol Lidar Infrared Pathfi nder Satellite Observ ations (CALIPSO), Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR) Ozone Monitoring Instrument (OMI), and Tropospheric Emission Spectrometer (TES) Jet Propulsion Laboratory (JPL) Langley Research Center (LRC)
National Oceanic and Atmos pheric Administration (NOAA) Office of Oceans and Atmospheric Research (OAR) Earth System Research Laboratory (ESRL) Pacific Marine Environmental Laboratory (PMEL) Atlantic Oceanographic and Me teorological Laboratory (AOML) Air Resources Laboratory (ARL) National Weather Service (NWS) National Environmental Satellite Da ta and Information Service (NESDIS) Office of Marine and Aviation Operations (OMAO) National Science Foundation (NSF) National Center for Atmo spheric Research (NCAR) Tennessee Valley Authority (TVA) United States Environmental Protection Agency (USEPA) Office of Air Quality Planning and Standards Air Quality Analysis Division Air Quality Modeling Group

Cooperating Research Universities Baylor University California Institute of Technology Chalmers University of Technology, Göteborg, Sweden Fort Hayes State University Georgia Institute of Technology Howard University Lamar UniversityFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 4 Texas Air Research Center (TARC) North Carolina State University Pennsylvania State University Portland State University Rice University Scripps Institution of Oceanography Texas A&M University Texas Tech University University of Alabama in Huntsville University of California at Los Angeles University of California at Santa Cruz University of Colorado Cooperative Institute for Research in Environmental Science (CIRES) University of Houston University of Manchester, Manchester, UK University of Maryland Baltimore County University of Massachusetts University of Miami University of New Hampshire University of Rhode Island University of Texas in Austin University of Washington Joint Institute for Study of Atmosphere Oceans (JISAO) Valparaiso University Washington State University

Cooperating Private Sector Organizations Aerodyne Research Inc. Baron Advanced Meteorological Systems Exxon Mobil Corporation Houston Regional Monitoring Network Science Systems and Applications, Inc. SensorSense, The Netherlands Shell Oil Corporation Sonoma Technology, Inc. URS Corporation

Other Cooperating Organizations
Houston Advanced Rese arch Center (HARC) Texas Environmental Research Consortium (TERC)Final Rapid Science Synthesis Report: Findings from TexAQS II

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### Executive Summary

The Rapid Science Synthesis Team (RSST) for the Second Texas Air Quality Study (TexAQS II) has been charged to address a series of 12 High Priority SIP-Relevant Science Questions identified by the Texas Commission on Enviro nmental Quality (TCEQ). Answers to these important questions were needed by TCEQ and ot her stakeholders in Texas to help fulfill the Commission’s responsibility to develop scientif ically sound State Implementation Plans (SIPs) by which to attain the recently implemented 8-hour National Am bient Air Quality Standard (NAAQS) for Ozone. SIPs for both the Houston- Galveston-Brazoria and the Dallas-Fort Worth Ozone Non-Attainment Areas were submitted to EPA in June 2007. This Final Report of the Rapid Science Synthesi s Team for TexAQS II is designed to provide statements of Findings in response to each of TCEQ’s 12 High Priority SIP-Relevant Science Questions. This report addresses: 1) Significant sources of ozone a nd aerosol pollution in eastern Texas, 2) Photochemical and meteor ological processes involved in the production, transport, and accumulation of these pollutants in various parts of Texas, 3) An assessment of the adequacy of emissions inventories for both biogenic and anthropogenic sources of ozone and aerosol precursor chemicals, and 4) An assessment of the skill of current air quality modeling and forecasting systems and recommendations for improvement of these systems. This Executive Summary provides a short introduc tion to the scientific Findings from TexAQS II research for use by TCEQ managers and other air-quality decision makers and stakeholders in Texas. It contains a complete list of the 12 High Priority SIP- Relevant Science Questions that TCEQ asked our Rapid Science Synthesis Team to address a series of carefully crafted statements of Findings that have been devel oped in response to each of these questions. We emphasize that the statements of scientific Findings developed as part of the Rapid Science Synthesis effort are based on analysis and inte rpretation of results from TexAQS II research within the limited time that was available be tween completion of the last TexAQS II field measurements on 15 October 2006 and our Rapid Sc ience Synthesis contract ending date – 31 August 2007. More thorough and comprehensive anal yses are already underw ay and will yield a great deal of additional important information in the future.

The institutional affiliations of the scientists responsible for the analyses leading to these Findings are given in the Ac knowledgments section, immediately preceding this Executive Summary. Each section of this re port is structured to contain one of the 12 SIP-Relevant Science Questions, then a numbered sequence of Findings in response to that question. Following each Finding, acknowledgment of the individuals whose anal yses and data contribu ted to that Finding are listed. It also provides a brief discussion of the evidence that supports each Finding. The concerned reader should carefully consider the caveats contained in these discussions. For the convenience of all readers, a glossary of acronyms is provid ed in Appendix 1. Please note that questions printed in blue were designated by TCEQ for special emphasis.Final Rapid Science Synthesis Report: Findings from TexAQS II

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### Findings

Question A Which local emissions are responsible for the pr oduction of high ozone in Houston, Dallas, and eastern Texas? Are different kinds of emission s responsible for transient high ozone and 8-hour-average high ozone (i.e., ≥ 84 ppbv)?

Finding A1 : The highest (i.e. > 125 ppbv) ozone co ncentrations in the HGB area result from rapid and efficient ozone formation in re latively narrow, concentrated plumes, which originate from HRVOC and NO x co-emitted from petrochemical facilities. The Houston Ship Channel (HSC) is the origin of the plum es with the highest ozone concentrations.

Finding A2a : Winds carry the emission plumes from the Ship Channel throughout the Houston area. The wind direction determines th e location of the ozone maximum relative to the Ship Channel; shifts in wind directio n lead to the transient high ozone events observed at monitoring sites within the Houston area.

Finding A2b : The general characteristics of the highest (i.e. > 125 ppbv) ozone formation in Houston did not change between 2000 and 2006, although the maximum observed ozone concentrations were lower in 2006 than in 2000.

Finding A3 : Emissions from the Houston Ship Cha nnel play a major role in the formation of the highest 8-hour average ozone concentrat ions (and ozone design values) observed in the Houston area.

Finding A4 : Observations during TexAQS 2006 sh owed that nitryl chloride (ClNO 2 ) is formed within the nocturnal boundary layer when NO x emissions and marine influences are both present. Following sunrise, ClNO 2 photolyzes to yield chlorine atoms, which may lead to earlier and more rapid O 3 production in the Houston region.



### Question B How do the structure and dynamics of the planetary boundary layer and lower troposphere affect the ozone and aerosol conc entrations in Houston, Dallas, and eastern Texas?

Finding B1: Boundary layer structure and mixing height near and over Galveston Bay and the eastern Houston ship channel area are spat ially complex and variable from day to day. Vertical mixing profiles often do not fit si mple models or conceptual profiles. High concentrations of pollutants are some times found above the boundary layer.

Finding B2 : Complex coastal winds, resulting from weak larger-scale winds over the area, occurred during many, but not all, ozone exceedance days in the Houston-GalvestonBrazoria nonattainment area. Almost no high-ozone days during TexAQS 2000 resembled any high ozone days from TexAQS 2006, but collectively the 2000 and 2006 field intensives sampled the full range of mete orological conditions associated with high ozone events.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 7 Finding B3 : After sea breeze days, the Houston plume was broadly dispersed at night through the formation of low-level jets.

Finding B4 : The Dallas ozone plume can extend well beyond the monitoring network.



### Question C Are highly reactive VOC and NO x emissions and resulting ambi ent concentrations still at the same levels in Houston as they were in 2000? How have they changed spatially and temp orally? Are there sp ecific locations where particularly large quantities of HRVOC are still being emitted? Are those emissions continuous or episodic? How well do the reported emissions inventorie s explain the observed concentrations of VOC and NO x ?

Finding C1 : There are indications that ethene (the lightest HRVOC) emissions from industrial sources in the Houston area decreased by 40 (±20)%, i.e., by a factor of between 1.25 and 2.5, between 2000 and 2006.

Finding C2 : Measurements of ethene emission flux es from petrochemical facilities during TexAQS 2006 indicate that the 2004 TCEQ po int source database underestimates these 2006 emissions by one to two orders of magnitude. Repeated sampling of the same petrochemical facility showed that the ethene emission flux remained constant to within a factor of two.

Finding C3 : Close to petrochemical HRVOC sources, the OH reactivity of propene is generally greater than that of ethene.

Finding C4 : The latest available emission inventor ies underestimate ethene emissions by approximately an order of magnitude.

Finding C5 : Inventories for NO x point sources at petrochemi cal facilities equipped with CEMS appear to be relatively accurate. Substantial decreases in NO x emissions in the Houston Ship Channel are suggested by the in ventories, and measurements from aircraft are qualitatively consistent with the NO x decreases.



### Question D What distribution of anthropogenic and biogeni c emissions of ozone and aerosol precursors can be inferred from observations?

Finding D1a : Several rural electric generation uni ts (EGU) in the Houston area and in eastern Texas have substantially decreased their NO x emissions per unit power generated since the TexAQS 2000 study. With one exception, SO 2 emissions have not changed appreciably since 2000 for the plants sampled in 2006.

Finding D1b : Comparisons of emissions derived fr om ambient observations with those measured by continuous emission monitoring systems (CEMS) indicate that the emissions from point sources equipped with CE MS are very accurately known.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 8 Finding D1c : Underreporting of CO emissions at se veral EGU noted in 2000 (Nicks et al., 2003) has been reconciled by large increases (by fa ctors of 5 to 50) in the inventory values between 2000 and 2006, as a result of newly impl emented CEMS monitoring of CO at these plants.

Finding D2 : On-road mobile emission invent ories developed from MOBILE6 have significant shortcomings. MOBILE6 consistently overestimates CO emissions by about a factor of 2. It accurately estimated NO x emissions in the years near 2000, but it indicates decreases in NO x emissions since then, while ambien t data suggest NOx emissions have actually increased. Consequently in 2006, NO x to VOC emission ratios in urban areas are likely underestimated by current inventories.

Finding D3 : Emissions from ships co nstitute a significant NO x source in the HGB region. Literature results provide accurate emis sion factors for inventory development.

Finding D4 : Mixing ratios of isoprene over Te xas measured from the WP-3D were used for evaluation of the BEIS-3 emission invent ory. On average, the isoprene emissions from the inventory and emissions derived from the measurements agree within a factor of ~2. There may be areas south of Dallas-Fort Worth and southwest of Houston, where isoprene emissions are lower than indicated by th e BEIS3 inventory based upon the biogenics emission land cover data (BELD-3.0).

Finding D5 : The speciation of VOC from mobile sources in the Houston and Dallas-Forth Worth areas agrees with detailed measurements in the northeastern U.S. However, initial results suggest that the agreement with the NEI-99 emission inventory is poor.



### Question E Are there sources of ozone and aerosol precurs ors that are not represented in the reported emissions inventories?

Finding E1: The observed mixing ratios and re gional distribution of ambient formaldehyde are broadly consistent with da ytime photochemical production from reactive VOC. An upper limit for prim ary formaldehyde emissions from mobile sources is obtained from nighttime measurements, and is small in comparison with the secondary, daytime formation.

Finding E2: Concentrated plumes of ammonia were ob served occasionally in the Houston Ship Channel area. These plumes often led to the formation of ammonium nitrate aerosol.

Finding E3: Concentrated plumes of gaseous elemen tal mercury from at least one point source were observed repeatedly in the Ho uston Ship Channel area and once in the Beaumont-Port Arthur area. The sources of the plumes could not be identified with current inventory sources of mercury.Final Rapid Science Synthesis Report: Findings from TexAQS II

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### Question F How do the mesoscale chemical environments (NOx-sensitive ozone formation vs radicalsensitive ozone formation) vary spatially and temporally in Houston, Dallas, and eastern Texas? Which mesoscale chemical environments are mo st closely associated with high ozone and aerosol?

Finding F1 : Both Eulerian and Lagrangian plum e modeling approaches indicate that in 2000 high ozone concentrations in the HGB area were sensitive to both VOC and NO x emission reductions.

Finding F2 : An observation-based approach to determine the sensitivities of high ozone in the HGB non-attainment area to the precursor VOC and NO x emissions has been investigated; it has yielded ambiguous results.

Finding F3 : At the highest ozone concentrations, the observed relationship between ozone and the products of NO x oxidation indicates less efficien t ozone production in the Dallas area than in the Houston area. In the obs ervation-based indicator species approach, this behavior corresponds to less NO x -sensitive and more VOC- or radical-sensitive ozone formation in Dallas compared to Houston.

Finding F4 : Tests of the ability of models to reproduce observed relationships between ozone and other photochemical products have the potential to provide very fruitful approaches to improving models.



### Question G How do emissions from local and distant sour ces interact to determine the air quality in Texas? What meteorological and chemical conditions exist when elevated background ozone and aerosol from distant regions affect Texas? How high are background concentrations of ozone and aerosol, and how do they vary spatially and temporally?

Finding G1: The maximum background ozone co ncentrations encountered in 2006 exceeded the 8-hour NAAQS. On average, air of continental origin had higher background concentrations than marine air. The average background ozone concentrations measured in 2006 in eastern Texas complement a previously developed climatology.

Finding G2: The net ozone flux transported out of Houston averages about a factor of two to three larger than the corresponding flux from Dallas. The fluxes from these urban areas are significant contributors to the backgr ound ozone in the eastern Texas region.

Finding G3 : Elevated background ozone concen trations for urban areas can include contributions from the recirculation of locally produced ozone or local precursor emissions.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 10 Finding G4 : Plumes from Texas urban areas make substantial contributions to the ozone, aerosol, and precursor concentrations in the rural regions of eastern Texas.

Finding G5 : Dust of African origin and sulfate aerosol advected into the Houston area, under southerly flow conditions from the Gulf of Mexico, can make significant contributions to the background aero sol in the eastern Texas region.

Finding G6: Nighttime chemistry influences the availability of oxides of nitrogen (NO x ), highly reactive VOC (HRVOC), and O 3 . Finding G7 : Low rural nighttime ozon e concentrations have been observed at some, but not all, rural locations in no rtheast Texas; these low nighttime ozone concentrations are not replicated in the regulatory modeling.



### Question H Which areas within Texas adversely affect th e air quality of non-attainment areas in Texas? Which areas outside of Texas adversely affect the air quality of non-attainment areas in Texas?

Finding H1 : Ozone can be transported into th e Dallas area from the Houston area.

Finding H2 : High ozone concentrations in eastern Texas result from both in-state sources and transport of continental air from the east and northeast.

Finding H3 : A synthesis of satellite and in si tu measurements with photochemical modeling and Lagrangian trajectory analys es provides a quantific ation of regional influences and distant sources on Houston and Dallas air quality during TexAQS 2006.

Finding H4 : In the Dallas area, local emission s and transport each contributed about equally to the average 8-hr ozone exceedance in 2002. Transported ozone alone can bring the Dallas area close to exceeding the 8-hour ozone standard.

Finding H5 : In the Houston area, local emission s and transport each contributed about equally to the average 8-hr ozone exceedances investigated by aircr aft flights in 2000 and 2006. As in Dallas, transported ozone alone ca n bring the Houston area close to exceeding the 8-hour ozone standard.



### Question I Why does the SAPRC chemical mechanism give different results than the carbon bond (CB-IV) mechanism? Which replicates the actual chemistry better?

Finding I1 : Air quality modeling for both 2000 and 2006 shows substantial differences in ozone concentrations predicted by the SAPRC99 and CB-IV chemical mechanisms.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 11 Finding I2 : In regions with very high VOC reactivity and high NO x emission density, differences in ozone formation and accumula tion predicted by regional photochemical models using the SAPRC99 and CB-IV mechan isms are due to differences in: (1) the chemistry of aromatics, especi ally mono-substituted aromatics (e.g., toluene), (2) nitric acid formation rates, and (3) the rates of free radical source terms in the SAPRC and CB-IV mechanisms.

Finding I3 : The differences in th e predictions of the SAPRC99 and CB-IV mechanisms can be probed using simulations of model co mpounds and comparisons of the simulations to environmental chamber data. Fo r simulations involving CO and NO x (inorganic chemistry), the predictions of the CB-IV a nd SAPRC99 mechanisms that are used in regional photochemical models (with no ch amber wall corrections) converge if the rate constants for the OH + NO 2 reaction are made consistent between the two mechanisms. The predictions of the Carbon Bond mechanis m, version 5 (CB05) also converge to the same predicted values if the OH + NO 2 rate constant is made consistent with the other mechanisms.

Finding I4 : The performance of the mechanisms in simulating olefins chemistry can be improved through more explicit representation of internal olefin chemistry, which has been added in CB05. In addition, performance of the SAPRC99 mechanism in simulating chamber data was improved for some experime nts when propene was modeled explicitly, as opposed to being represented by a lumped chemical species.

Finding I5 : For high reactivity chamber experiment s involving olefins, sensitivity analyses indicated that mechanism adjustments that wo uld lead to increased radical concentrations (increasing the radical yield in olefin -ozone reactions and changing the OH+NO 2 termination rate constant) had little impact on predicted ozone concentrations.

Finding I6 : The SAPRC99 mechanism performed better than the CB mechanisms in simulating some chamber exp eriments with toluene; the mechanism performances were more comparable for xylenes and other multiply substituted aromatics.

Finding I7: The differences between the SAPRC and CB-IV mechanism predictions for toluene chemistry decrease substantially if th e yield for the lumped species CRES, and rate constant for the OH + NO 2 , are made consistent between the two mechanisms. The predictions of the CB05 mechanism also conver ge to the same predicted values if the CRES yield and the OH + NO 2 rate constant are made consistent with the other mechanisms.

Finding I8: For simulations of ambient surrogate mixture experiments in the UCR EPA chamber, all mechanisms underpredict O 3 at low ROG/NO x ratios, with the bias decreasing as the ROG/NO x ratio increases. In simulations of mixture experiments in chambers without aromatics, CB05 performs the best, and with no dependence of bias on the ROG/NO x ratio.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 12 Finding I9 : SAPRC99, CB-IV, and CB05 all successful ly predicted concentrations of ozone and other species during simulation of a stagnati on event, if the chemical mechanisms were initialized after initial high co ncentrations of olefins had reacted. None of the mechanisms successfully predicted a rapid rise in radi cal concentrations and ozone concentration concurrent with initially high C2, C3, and CM BO concentrations during the event.



### Question J How well do air quality forecast models predict the observed ozone and aerosol formation? What are the implications for improvement of ozone forecasts? Finding J1a : Most forecast models exhibit skill in predicting maximum 8-hr-average O 3 but none of the models is better than p ersistence in predicting 24-hr-average PM 2.5 levels.

Finding J1b : Seven air quality forecast models a nd their ensemble were generally unable to forecast 85 ppbv 8-hr-average O 3 exceedances with any reliability.

Finding J2 : Sophisticated data assimilation of meteorological and even chemical observations is essential for improving photochemical model forecasts.

Finding J3 : Model performance evaluation s and intercomparisons require a comprehensive, best-guess emi ssions inventory for the TexAQS 2006 Field Intensive.



### Question K How can observation and modeling approaches be used for determining (i) the sensitivities of high ozone in the HGB non-attainment area to the precursor VOC and NO x emissions, and (ii) the spatial/temporal varia tion of these sensitivities?

Finding K1: A simple, heuristic model based upon the Empirical Kinetic Modeling Approach (EKMA) method suggests that the HGB region may ultimately require drastic NO x emissions controls to reach comp liance with the NAAQS for ozone.

Finding K2: The same model suggests that in a proj ected future scenario with very strict VOC emission controls, but without drastic NO x emission controls, biogenic VOC emissions plus background con centrations of CO and methan e may be sufficient to cause ozone exceedances.



### Question L What existing observational databases are suit able for evaluating and further developing meteorological models for application in the HGB area? The Final Report for Question L can be found in Appendix 2 of this Report, as submitted 31 October 2006.Final Rapid Science Synthesis Report: Findings from TexAQS II

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### Introduction

The Rapid Science Synthesis Team (RSST) for the Second Texas Air Quality Study (TexAQS II) was charged to address the series of 12 High Prio rity SIP-Relevant Science Questions listed in the text box below. The Texas Commission on Environmental Quality (TCEQ) posed these questions to elicit responses needed to help TCEQ fulfill its responsibility to develop State Implementation Plans (SIPs) by which to atta in the recently implemented 8-hour National Ambient Air Quality Standards (NAAQS) for ozone in the 8-county Houston-GalvestonBrazoria and 8-county Dallas-Fort Worth ozone non-attainment ar eas. TCEQ’s interests and questions also deal, to a lesser ex tent, with fine particulate matter. This report presents the final Responses of the RSST to these 12 TCEQ Science Questions. As part of the Rapid Science Synthesis effort , TCEQ and the Office of the Director for the Southern Oxidants Study (SOS-OD) establis hed Working Groups to address each Science Question. Each Working Group consisted of 8-15 experts drawn from vari ous university-, state-, federal-, and private-sector orga nizations. The members of the Wo rking Groups are listed in the text box below; contact informa tion is given in Appendix 3. The Working Groups developed research approa ches for responding to each of the 12 TCEQ Science Questions; the approaches are describe d in a Progress Report from the RSST dated 31 July 2006. On 12 and 13 October 2006, shortly before completion of the TexAQS II Field Study, the Rapid Science Synthesis Team partic ipated in a day-and-a half-long meeting in Austin, Texas. Presentations from this initia l Rapid Science Synthesis Workshop were posted on the TCEQ website, http://www.tceq.state.tx.us/imple mentation/air/airmod/texaqsfiles/TexAQSII.html . These presentations formed the basis for an RSST report, Preliminary Findings from the Second Texas Quality Study (TexAQS II) , dated 31 October 2006 [8 November revision]. The RSST participated in the Principal Finding s and Data Analysis Workshop for TexAQS II/ GoMACCS 2006, held 29 May – 1 June 2007, in Au stin, Texas. Presentations from this Workshop were also post ed on the TCEQ website, http://www.tceq.state.tx.us/implementati on/air/airmod/committee/scc.html#workshops . These presentations and subsequent analyses of res earch results by various TexAQS II researchers provided the foundation for this final repor t of the Rapid Science Synthesis Team, Final Rapid Science Synthesis Report: Findings from TexAQS II . The sections of this Final Report present carefully crafted statements of scientific findi ngs in response to each of TCEQ’s High Priority SIP-Relevant Science Questions. In this connection, we found very helpful the “Guidelines for Formulation of Scientific Findings to be Us ed for Policy Purposes” shown in Appendix 4. Electronic copies of the three RSS reports to TCEQ are available on websites maintained by TCEQ (listed above) and by the National Ocean ic and Atmospheric Administration (NOAA) ( http://esrl.noaa.gov/csd/2006/ ). The following introductory information provides some background and perspective for the Science Questions and the Responses developed by the RSST.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 14 TCEQ’s High Priority SIP-Relevant Science Questions and Leaders (L), Participants (P) and Observers (O) in Working Groups wi thin the Rapid Science Synthesis Team

A Which local emissions are responsible for the production of high ozone in Houston, Dallas, and eastern Texas? Are different kinds of emissions responsible for transient high ozone and 8- hour-average high ozone (i.e., ≥ 84 ppbv)? L – David Parrish, P – Tom Ryerson, Joos t deGouw, Basil Dimitriades, David Allen, Mark Estes, Bernhard Rappenglück, O – Noor Gillani

B How do the structure and dynamics of the planetary boundary layer and lower troposphere affect ozone a nd aerosol concentrations in Houston, Dallas, and eastern Texas? Co-L – Robert Banta & John Nielsen-Ga mmon, P – Allen White, Christoph Senff, Wayne Angevine, Bryan Lambeth, Lisa Da rby, Bright Dornblaser, Daewon Byun, Bernhard Rappenglück, O – Carl Berkowitz, Noor Gillani

C Are highly-reactive VOC and NO x emissions and resulting ambient concentrations still at the same levels in Houston as th ey were in 2000? How have they changed spatially and temporally? Are there speci fic locations where particularly large quantities of HRVOC are sti ll being emitted? Are those emissions continuous or episodic? How well do the reported emi ssions inventories ex plain the observed concentrations of VOC and NO x ? L – David Parrish, P – David Allen, Joos t deGouw, Tom Ryerson, Mark Estes, David Sullivan, John Jolly, Eric Williams, Barry Lefer, O – Yulong Xie, Carl Berkowitz, Noor Gillani. Note: To answer the last part of question C, TCEQ must define the inventory to which the observations must be compared.

D What distribution of anthropogenic and bi ogenic emissions of ozone and aerosol precursors can be inferred from observations? Co-L – David Allen & David Parrish, P – Tom Ryerson, Charles Brock, Joost deGouw, David Sullivan, Mark Estes, John Jolly, Eric Williams, Barry Lefer, Bernhard Rappenglück, O – Yulong Xie, Carl Berkowitz, Noor Gillani

E Are there sources of ozone and aerosol pr ecursors that are not represented in the reported emissions inventories? L – David Parrish, P – Tom Ryerson, Charles Brock, Joost deGouw, David Sullivan, John Jolly, David Allen, Eric Williams, Barry Lefer, Bernhard Rappenglück

F How do the mesoscale chemical environments (NO x -sensitive ozone formation vs radical-sensitive ozone formation) vary spatially and temporally in Houston, Dallas and eastern Texas? Which mesos cale chemical environments are most closely associated with high ozone and aerosol? Co-L – Basil Dimitriades & David Parri sh, P – David Allen, Harvey Jeffries, William Vizuete, Daewon Byun, Mark Estes, Kenneth Schere, Barry Lefer, Bernhard Rappenglück, O – Yu long Xie, Carl Berkowitz

Note: Letter designations are for convenience only and do not denote priority. Questions in blue have been designated by TCEQ to receive special emphasis.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 15 TCEQ’s High Priority SIP-Relevant Science Questions and Leaders (L), Participants (P) and Observers (O) in Working Groups wi thin the Rapid Science Synthesis Team (continued)

G How do emissions from local and distant sources interact to determine the air quality in Texas? What meteorological and chemical conditions exist when elevated background ozone and aerosol from distant regions a ffect Texas? How high are background concentrations of oz one and aerosol, and how do they vary spatially and temporally? Co-L – David Allen & David Parrish, P –Bryan Lambeth, David Sullivan, Basil Dimitriades, Charles Brock, Michael Hardesty, Steve Brown, Joost deGouw, Bernhard Rappenglück, Brad Pierce, Wa llace McMillan, Kevin Bowman, David Winker, Tim Bates

H Which areas within Texas adversely affect the air quality of non-attainment areas within Texas? Which areas outside of Te xas adversely affect the air quality of non-attainment areas within Texas? Co-L – David Allen & David Parrish, P – Mark Estes, Greg Yarwood, Basil Dimitriades, David Sullivan, Charles Br ock, Michael Hardesty, John Jolly, Bryan Lambeth, Brad Pierce, Wallace McM illan, Kevin Bowman, David Winker

I Why does the SAPRC chemical mechanism give different results than CB-IV? Which replicates the actual chemistry better? Co-L – David Allen & Greg Yarwood, P – Ha rvey Jeffries, William Vizuete, Bill Carter, David Parrish, Stuart McKeen, Daewon Byun, Joost deGouw, Barry Lefer, Bernhard Rappenglück, O – Mark Estes, Noor Gillani

J How well do forecast air quality models predict the observed ozone and aerosol formation? What are the implications fo r improvement of ozone forecasts? L – Stuart McKeen, P – Gregory Carmic hael, Bryan Lambeth, Kenneth Schere, James Wilczak, Greg Yarwood, Daewon Byun, John Nielsen-Gammon, Michael Hardesty

K How can observation and modeling approach es be used for determining (i) the sensitivities of high ozone in the HGB non-attainment area to the precursor VOC and NOx emissions, and (ii) the spatial/tem poral variation of these sensitivities? Co-L – Basil Dimitriades & David Parrish, P – Ted Russell, Harvey Jeffries, William Vizuete, Mark Estes, David Sullivan, Tom Ryerson, Greg Yarwood, Barry Lefer, Bernhard Rappenglück, O – Noor Gillani

L What existing observational databases are suitable for evaluating and further developing meteorological models for application in the HGB area? L – Lisa Darby, P – Robert Banta, John Nielsen-Gammon, Daewon Byun, Wayne Angevine, Mark Estes, Brya n Lambeth, Stuart McKeen

Note: Letter designations are for convenience only and do not denote priority. Questions in blue have been desi g nated b y TCEQ to receive s p ecial em p hasis.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 16 Non-attainment Areas Requiring SIP Development TCEQ initiated TexAQS II to obtain additional SI P-relevant understanding of the processes that lead to formation and accumulation of ozone and particulate matter in two very different ozone non-attainment areas within Texas: 1) The Houston-Galveston-Brazoria (HGB) ozone non-attainment area is a coastal urban region of about 5 million people. It consists of eight counties in southeastern Texas and is subject to distinctive coastal (sea-b reeze) meteorological conditions and large petrochemical sources of industrial emissi ons, especially the Houston Ship Channel (HSC) and other nearby industrial sites. In 2005 HGB was classified as a non-attainment area of “moderate” status, with an attainme nt deadline of June 2010. The most recent SIP revision (June 2007) does not demonstrate attainment by that date, and the state of Texas has requested a reclassi fication of the HGB non-attainme nt area to “severe,” which requires an attainment date of June 2019 and a SIP revision deadline of March 2010. HGB is in attainment of the PM 2.5 NAAQS. 2) The Dallas-Fort Worth (DFW) ozon e non-attainment area is an inland urban region, also of about 5 million people. The DFW non-atta inment area includes 8 counties in northcentral Texas, with relatively typical inla nd metropolitan meteor ological conditions and only limited industrial sources w ithin the non-attainme nt counties, but with several power plants in nearby locations within northeastern Texas. DFW also was classified as in “moderate” non-attainment of the ozone standa rd, with a June 2010 a ttainment date. The June 2007 SIP revision submitted to EPA demonstr ates attainment of the 8-hour standard by that deadline. DFW is in attainment of the PM 2.5 NAAQS.

Recent Air Quality Studies in Texas During recent years, the State of Texas provided substantial fundi ng for scientific studies to improve understanding of ozone formation and accumu lation in eastern Texas. These scientific studies have been focused around two majo r air-quality field research programs.

First Texas Air-Qualit y Study (TexAQS 2000) TexAQS 2000 was a relatively short-term (six-w eek-long) intensive field measurement program conducted during the summer of 20 00 that included aircraft-bas ed, tall tower-based, and groundbased field measurements; the study period was imbedded in a 16-month long study on particulate matter in the Houston area. Th e research results demonstrated that the extraordinarily stringent decrease in NO x emissions proposed in the 2000 SIP for the HGB nonattainment area of Texas was not an optimal appro ach; a more realistic plan for attainment of the NAAQS for ozone should involve decreases in emissions of both VOC and NO x – with a particular focus on the highly reactive VOC (HRVOC) emissions in the industrial areas surrounding the Houston Ship Channel and Galveston Bay.

Second Texas Air Quality Stud y (TexAQS II and TexAQS 2006) TexAQS II was a much longer-term (18-month-l ong) program focused on the photochemical and meteorological processes leading to the forma tion and accumulation of ozone and particulate matter air pollution in eastern Texas. The study began in June 2005 and extended through 15 October 2006. This period includes not only th e 2005 and 2006 summer ozone seasons, but also the intervening fall, winter, and spring months when occasional exceedances of the 8-hour ozone standard did occur, both together with and separately from, occasional episodes of highFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 17 concentrations of airborne particulate matter. The field measurements of TexAQS II constitute one of the most comprehensive air quality field research studies ever undertaken in the United States; the analysis and interpretation phases of TexAQS II will extend for years after completion of the field measurements. TexAQS II culminated during the months of August, September, and October 2006 with an intensive series of coordinated chemical and me teorological measurements and modeling studies. In order to distinguish this relatively short-term but very intensive study from the earlier parts of TexAQS II, the 2006 summer intensive study has been dubbed TexAQS 2006. The air-quality measurement platfo rms during TexAQS 2006 included: 1) Multiple instrumented aircraft operating in both the HGB and DFW areas of Texas, 2) A series of ground-based sites with continui ng measurements of air chemistry, a network of ground-based wind profiler and rawinsonde lo cations, a mobile solar occultation flux research van, and both aircraft-based and ship-based ozone and aerosol lidar measurements throughout eastern Texas, 3) NOAA’s Ronald H. Brown Research Vessel operating with in the Houston Ship Channel and Galveston Bay, 4) The 200-foot-tall Moody Tower at the University of Houston, and 5) The National Aeronautics and Space Admini stration (NASA) Aura, Aqua, Terra, and GOES satellites. The multimillion-dollar 18-month-long TexAQS II field research study and its embedded shortterm intensive study, TexAQS 2006, were conducted jointly by staff of TCEQ and by scientists and engineers working under contracts issued by TCEQ, the Texas Environmental Research Consortium (TERC), and through formal and/or in formal agreements for cooperation with the many other research organizations listed in the Acknowledgments of this Report. All the research studies and plans for analysis and interpretation of results obtained during TexAQS II and TexAQS 2006 were undertaken with specific scientific re search objectives in mind. But many of these investigations also were designed, undertake n, and funded by various federal, state, and private-sector organizations with specific po licy purposes in mind – and most particularly in order to be used by TCEQ in developing State Implementation Plans that were required by the US EPA in 2007 for the Houst on-Galveston-Brazoria and Dallas-Fort Worth ozone non-attainment areas. The very limited time available between completion of many of the TexAQS II field measurements and the deadline fo r preparation and final submission of the SIPs required for Houston and Dallas-Forth Worth was extraordinarily short – thus the need for a rapid synthesis of SI P-relevant science.

History of Ozone Exceedances in Houston and Dallas Figure 1 shows that maximum ozone concentrat ions throughout HGB and DFW decreased from 1978 to 2005. It is notable that: 1) Most of the decreases in HGB occurred before 1990; 2) The decreases were greater in HGB (fro m over 150 ppbv to near 100 ppbv) than in DFW

(from about 120 ppbv to near 95 ppbv); and 3) The ozone design values in HGB are now appr oximately the same as in DFW. The large

variability in ozone maxima since 1990 – bot h among monitoring sites and between years – make it difficult to determine whether these apparent decreases in ozone maxima since 1999 are due to changes in emissions or to meteorological variations.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 18 Although the ozone design values for the HGB a nd DFW areas are now very similar, acute ozone episodes are much more frequent in Houston than Dallas. During 2000-2005, the HGB area had 219 8-hour ozone exceedance days while the DFW area had 203. In contrast, however, during this same period, the HGB area had 147 one- hour exceedance days (ozone concentrations Figure 1 . Temporal trends of 8-hour O 3 design values at a) HGB and b) DFW monitoring sites. The 8-hour O 3 design value for each monitor is the 3-year average of the fourth-highest daily maximum 8-hour-average ozone concentration measured at that monitor; these values are representative of the maximum ozone observed and the air concentrations of specific concern in developing control strategies. (Figure from Bryan Lambeth, TCEQ)Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 19 > 120 ppbv) but the DFW area had only 24 such da ys. Over 60 percent of the HGB 8-hour exceedances were accompanied by 1-hour exceedances. In the HGB area the local design value maxima have not changed locations during that peri od, and have persisted in the vicinity of Deer Park, Bayland Park, and Aldine. There is a local minimum in ozone design value at sites located in the urban core of Houston, presumably becau se of the abundant fresh NO emissions there, which can suppress ozone formation, or titr ate ozone transported into the area.

Regional Character of Ozone Exceedances in Eastern Texas Maximum daily ozone concentrations generally o ccur in multi-day episodes, and episodes often are region-wide, with similar patterns throughout eastern Texas (Figure 2). These region-wide episodes of high ozone concentrations are seen in all years. Two hypotheses can be suggested to explain this behavior: 1) The episodes occur during meteorologica l conditions under which elevated ozone

concentrations, produced in upwind regions, are transported into the whole region of eastern Texas. 2) The high ozone episodes represent periods wh en the regional meteorology is particularly

conducive (sunny, hot, stagnant) to local or regional formation of ozone from locally or regionally emitted precursors, and the intervening periods represent meteorological conditions that are particular ly poor (cloudy, cool, windy) for ozone formation within the region.

Figure 2 . Daily maximum 8-hour ozone averages for August-September 2004 for all monitoring sites in eastern Texas. Each column represents one day, and each row represents one monitor within each community (TLM-Tyler, Longview, Marshall; BPA-Beaumont, Port Arthur; Aus-Austin; SAo-San Antonio, CCV-Corpus Christi, Victoria). The increasing color intensity indicates increasing ozone concentrations (yellow ≥ 65 ppbv, orange ≥ 85 ppbv, red ≥ 105 ppbv, purple ≥ 125 ppbv. (Data from TCEQ; Figure adapted from David Sullivan-U. Texas)Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 20 A decreasing temporal trend in regional ozone concentrations and exceedances has occurred over the past 13 years. A trend analysis covering 1994 to 2004 sh ows that the average of the maximum 8-hour-average ozone concentrations observed at upwind surface monitoring stations in the Houston area have declin ed since about 2000 (Figure 3). Values were approximately 80 ppbv in the 1990s and have dropped to between 60 and 70 ppbv in 2002 – 2004. This suggests that background ozone concentrations during high ozone episodes in the Houston area have been declining in recent years. There has been a concomitant decrease in the total number of exceedance days recorded each year in August a nd September at twelve representative eastern Texas monitoring sites (Figure 4). These sites we re selected for complete ness of data coverage (>75% each year) and regional representation (one site per county, except for two in Harris County). It is important to not e that the year of the TexAQS 2006 field study had the fewest number of regional exceedances during this 13-year period.

Figure 4 . Total number of monitordays with maximum 8-hour-average ozone above 85 ppbv in August and September during the years between 1994 and 2006. Twelve regional monitoring sites in eastern Texas were considered in this analysis. (Data from TCEQ; Figure from David Sullivan-U. Texas) Figure 3 . 1994 to 2004 trend of highest 3 - 6 values of 8-hour background ozone from upwind surface stations in Houston area (from Nielsen-Gammon et al., 2005 )

.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 21 An Important Distinction: Ozone Production versus Accumulation From the standpoints of understanding air quality sc ience and the practical business of air quality management, it is important to reco gnize the distinction between ozone formation and/or ozone production (which are essentially synonymous)

, on the one hand, and ozone accumulation on the other hand. The ambient concentration of ozone in a particular, near-surface volume of air at a fixed location is the net result of six proce sses that occur in the lower atmosphere:

• In situ ozone formation (or production) from chemical precursors in an air parcel,

• In situ ozone destruction by chemical reactions in that air parcel,

• Deposition of ozone from the air to ve getation or other surfaces,

• Vertical transport of ozone from an ozone reservoir aloft,

• Horizontal transport of ozone from up-wind locations, and

• Dispersion and dilution as a result of mixing with cleaner air during advection or vertical mixing when the height of the pl anetary boundary layer increases. In effect, the rate of change in the concentrati on of ozone in a particular , near-surface volume of air is an “algebraic sum” of th e rates of all six of these proces ses. The time-integral of this “algebraic sum” then represents the accumulation of ozone in that volume of air at that specific location (if the integral is positive, or a decrease in ambient ozone concentration if the integral is negative.) Atmospheric scientists and engineers are we ll aware of these important processes and distinctions. They devote much ef fort to experimentally determining the rates of each of these processes, and to developing models th at accurately calculate the resulting accumulation of ozone to compare with ambient observa tions of ozone concentrations. Air quality managers are concerned with mana gement of near-surface ozone concentrations; thus, they are not concerned with the production of ozone as such, so long as the ambient concentration of ozone does not exceed the NAAQS. Air quality managers are required to develop State Implementation Plans that are aimed at keeping ozone from accumulating in the atmosphere in concentrations that exceed these NAAQS. In the mixed dialog that occurs between atmospheric scientists and air quality managers, this important distinction can be lost , and a degree of confusion may re sult. Some of this confusion arises because, among the six processes listed above, only the rate of ozone formation can be effectively addressed through local emi ssion control efforts. Thus, ozone formation receives particular attention from both atmospheric scientists and air quality managers. In this report, the term “ accumulation of ozone” is used when the distinction is important. However, in many situations, the distinction between “ accumulation of ozone” and “ ozone production ” is not so important. These situ ations arise when the rate of production dominates the “algebraic sum” above. In such s ituations, the discussion will focus on “ ozone production ” and the contribution of the other five processes will not be discussed.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 22 Reactivity of VOC Ozone Precursors Photochemical production of ozone requires two classes of precursors: oxides of nitrogen (NO x , which include NO and NO 2 ) and volatile organic compounds (VOC). During ozone formation the VOC are photochemically oxidized while NO x catalyzes the formation of ozone during that oxidation. Oxidation of carbon monoxide (CO) and methane also can contribute significantly to the formation of ozone. Figure 5 shows the rate constants for the reactions of many different VOC with the hydroxyl radical (OH). This radical is the active agent in the atmosphere that initiat es the photochemical oxidation of VOC. The OH concentration in ambient air varies widely, but a represen tative, 24-hour average value is 1 x 10 6 OH radicals per

cm 3 of air. The right hand axis in Figure 5 gives the lifetimes of various VOC at that representative OH concentration. The OH rate c onstants span such a wi de range that the VOC lifetimes vary from many days to less than one hour. The effectiveness of any given VOC in ozone formation is approximately proportional to its OH reactivity, whic h is the product of the VOC concentration times its OH rate constant. The TexAQS 2000 study highlighted the importa nt role played by highly reactive VOC (HRVOC) in the HGB area. These species are the alkenes, five of which are included in Figure 5 (above the “Alkenes” label in the figure). Their importance is due to their high OH reactivities, which result from both their larg e OH rate constants and their high ambient concentrations. The high ambient concentrations result from large quantities of HRVOC emitted from petrochemical facilities in the HGB area. The biogenic VOC, especially isopre ne, are particularly important in forested regions of eastern Texas (Figure 6). Recently, new land cover a nd vegetation data have improved the spatial resolution and provided more qua ntifiable estimates of uncerta inties for these emissions. Biogenic emissions derived from these new data ar e about 40% lower than previous calculations. It is important to recognize that some biogenic VOC, especially isoprene, are also emitted from anthropogenic sources in the indus trial facilities in HGB. Figure 5 . Rate constants and representative atmospheric lifetimes for some VOC. CO is included for comparison. Rate constant data are from the literature.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 23 Figure 6 . Biogenic VOC emissions in southeast Texas on a representative summer day. The emissions are calculated at 4 km x 4 km spatial resolution based on the latest University of Texas-Center for Space Research land cover data. (Figure adapted from Mark Estes, TCEQ)

A Reflective Note Although this is the Final Report of the Rapid Sc ience Synthesis Team, all of us on the RSST recognize that we have not ye t derived maximum benefit from all the research data and information that was accumulated during TexAQS II. The accelerated pace of our analyses and interpretations did not allow full utilization of th e results. Fortunately, however, the future will provide many additional opportunities for more detailed analyses, for more intercomparisons of results, and for more integrative interpretations. The findings developed in this Final Report will be subject to more rigorous examination when TexAQS II results are presented at scientific meetings and when papers deriving from TexAQS II research are submitt ed for publication in refereed scientific journals. We are proud of what has been accomplished so far – the new scientific insights obtained, and the new policy insights provided. We hope this Final Report will be useful to TCEQ and the people of Texas, as they conti nue the practical business of air quality management in Houston and Dallas-Fort Worth. We wish to express our thanks to the many st aff in TCEQ who organi zed our several workshop meetings and helped us gain access to additiona l data and information from existing databases – especially those that go back in time to obs ervations made long before both TexAQS 2000 and TexAQS II were conceived and implemented. We have also enjoyed the excellent cooperation among so many of the diverse groups of scientists , engineers, graduate students, and post-docs that joined in various parts of this very large study. It was fa scinating to have the chance to understand more fully the very challenging chemi cal, biological, physical, meteorological, and mathematical factors that are i nvolved, as well as some of th e social, cultural, industrial, political, professional, and economic forces involve d in managing air quality – especially in the Houston-Galveston, and Brazori a ozone non-attainment area.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 24 

### Response to Question A

QUESTION A Which local emissions are responsible for the pr oduction of high ozone in Houston, Dallas, and eastern Texas? Are different kinds of emission s responsible for transient high ozone and 8-hour-average high ozone (i.e., ≥ 84 ppbv)?

BACKGROUND Question A is related to Ques tions F and K, which focus on the sensitivities of high concentrations of ozone to VOC and NO x emissions. Here the res ponse to Question A gives an overview of the photochemical processes that produce the highest observed ozone concentrations, and the emissions that allow thos e processes to proceed rapidly. These highest ozone concentrations are limited to the HGB region, so the focus is on that area. Understanding 8-hour-average high ozone requires very different considerations and approaches than the understanding of transient high oz one; an initial inve stigation of the longer-term-average ozone in HGB is included.

FINDINGS

Finding A1 : The highest (i.e. > 125 ppbv) ozone co ncentrations in the HGB area result from rapid and efficient ozone formation in re latively narrow, concentrated plumes, which originate from HRVOC and NO x co-emitted from petrochemical facilities. The Houston Ship Channel (HSC) is the origin of the plum es with the highest ozone concentrations. Analysis: Trainer-NOAA; Data: R yerson, de Gouw et al.-NOAA, Fried et al.-NCAR, Atlas et al.- U. Miami. The aircraft flights of TexAQS 2000 and 2006 rev eal a persistent featur e over Houston – on all days conducive to photochemical ozone producti on, narrow and concentrated plumes of ozone formed and were transported downwind from th e HSC. Figure A1 shows measurements from one flight; on this day the wind was from the east-northeast. The top two graphs show the measurements of two prim ary ozone precursors. NO y represents the NO x emissions plus their oxidation products, primarily PAN and nitric aci d. Ethene, a HRVOC, is an important ozone precursor. The concentrations of these two pr imary emissions were highest over the HSC, and defined a narrow downwind plume with decreasing c oncentrations. Figure A1 indicates that the NO x and ethene emission sources are not precisel y collocated, but the primary sources of both are in the HSC region. The downwind d ecrease in the concentration of NO y was primarily due to dilution. Clearly, ethene concentrati ons decreased more rapidly than NO y , due to its much faster removal through photochemical oxidation. The bo ttom two panels of Figure A1 show the concentrations of two secondary photochemi cal products, ozone and formaldehyde, formed within the plume of primar y emissions. Formaldehyde, produced by the photochemical oxidation of ethene and other HRVOC, formed during the downwind tr ansport as the HRVOC were oxidized; formaldehyde itself is an importa nt ozone precursor. Ozone accumulated as a concentrated (up to 147 ppbv), narrow plum e within the much less concentrated ( ≤ 100 ppbv), but wider ozone plume from the larger Houston urban area.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 25 -96.5 -96.0 -95.5 -95.0 -94.5 16 12 8 4 CH 2 O (ppbv) 12 8 4 0 C 2 H 4 (ppbv) 30.5

30.0

29.5

29.0

28.5 40 30 20 10 0 NOy (ppbv)

30.5

30.0

29.5

29.0

28.5 -96.5 -96.0 -95.5 -95.0 -94.5 60 90 120 150 O

3 (ppbv)

Figure A1 . Measurements of NOy, ethene (C 2 H 2 ), ozone, and formaldehyde (CH 2 O), plotted along the flight track of the WP-3D on 6 October 2006. The symbols are sized and color-coded according to the indicated concentrations. Only measurements be low 1.5 km altitude are shown. (A close approach to the Parish power plant has been removed from the NOy plot.) The high temporal resolution measurements of ethene made by the laser photo-acoustic spectrometer (LPAS) and the wide species c overage of VOC measurements conducted on the canisters from the whole air sampler (WAS) pr ovided detailed characterization of the HRVOC sources in HSC. The map in Figure A2 shows the concentration of ethene measured by these two systems downwind from six petrochemical comple xes; the plumes of ethene emissions from the complexes are clearly delineated. The four pie charts in Figure A2 indicate the total OH reactivity of the VOC in four of the WAS samp les, and show how that reactivity was divided among four of the lightest HRVOC sp ecies, and the total of all of the other alkanes, aromatics, and biogenic VOC measured in the canister sample s. These four canister samples illustrate the general finding that the HRVOC dominate the VOC reactivity immediat ely downwind of the petrochemical facilities, and thus are responsible for a la rge fraction of the high ozoneFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 26 production in the HGB area. The total reactivity and the fraction of the reactivity contributed by the HRVOC decrease downwind in the plumes, as expected from their very short lifetimes. The pie charts in Figure A2 do not include aldehydes. These oxygenated VOC, which also account for a significant fraction of the OH reactivity, are primarily secondary products of the oxidation of the HRVOC. Thus, the contribution of the aldehydes to ozone production increases the fraction of the total VOC reactiv ity in the HSC area that is attributable to the emissions of the HRVOC.

Figure A2 . Measurements of ethene along the section of the WP-3D flight track included in the rectangle in the upper right panel of Figure A1. The symbols are sized and colored according to the indicated concentrations. The triangles indicate the 20-second-average measurements by LPAS, and the circles indicate the measurements from WAS. Th e pie charts are sized according to the total OH reactivity for four of the WAS samples, and the co lors indicate the OH reactivity contributed by the four indicated HRVOC.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 27 Finding A2a : Winds carry the emission plumes from the Ship Channel throughout the Houston area. The wind direction determines th e location of the ozone maximum relative to the Ship Channel; shifts in wind directio n lead to the transient high ozone events observed at monitoring sites within the Houston area.

Finding A2b : The general characteristics of the highest (i.e. > 125 ppbv) ozone formation in Houston did not change between 2000 and 2006, although the maximum observed ozone concentrations were lower in 2006 than in 2000. Analysis: Ryerson et al.-NOAA; Data : Ryerson, Williams et al.-NOAA. Ryerson et al. (2006) examined the four Electra flight s during TexAQS 2000 that encountered ozone concentrations above 150 ppbv. Figure A3 shows the flight track segments where the highest ozone concentrations were observed. In each case, back-trajectory analysis attributed these high ozone concentrations to plumes from industrial emission sources in the HSC area. Measured chemical characteristics of the plumes (simultaneous high NO x , SO 2 , CO 2 , and oxidation products of HVROC) confirmed this sour ce attribution. The relationship between the transport times derived from the trajectory anal ysis and the observed enhancements in ozone concentration provided a measure of the net av erage ozone production rates in the plumes. Figure A3 also shows the observed relations hip between ozone and the products of NO x oxidation for those four flights; the slopes of these relationships provide an estimate of the net ozone production efficienci es in these plumes.

Figure A3 . Highest ozone (red points) observed by the Electra aircraft during four flights in TexAQS 2000. In the left panel flight track segments are color-coded by observed ozone, and in the right panel the dependence of ozone on the products of NO x oxidation are shown with approximate ozone production efficiencies estimated from fitted slopes (Ryerson et al., 2006).Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 28 Figure A4 presents a similar analysis fo r the three WP-3D daytime flights and the RHB ship cruise segment during TexAQS 200 6 that encountered ozone con centrations near or above 120 ppbv. A simple wind direction analysis, coupled with the chemical plume signatures, indicate that in each case these plumes al so originated from industrial emission sources in the Houston Ship Channel area. Figure A4 also shows th e relationship between ozone and the products of NO x oxidation in these plumes.

Comparison of Figures A3 and A4 show that th e ozone production environment in the Houston area was similar in 2000 and 2006. Similar ozone vs. (NO y -NO x ) slopes are seen in each year; the maximum concentrations of ozone observed in both years were associated with slopes near 7. Lower maximum ozone concentrations were obs erved in 2006, but this difference may be partially due to meteorological factors; the bac kground ozone, as indicated by the y-intercepts in the two right panels in Figures A3 an d A4, was lower in 2006 and lower background concentrations led to lower maximum concen trations. The lower 2006 background may indicate smaller effects of stagnation and recircula tion that year. Finally, the maximum ozone concentrations were observed at greater dist ances from the HSC in 2006 than in 2000 (note difference in area covered in the two maps). This difference may be attri butable to slower ozone production or to greater transport speeds in 2006 than in 2000. Figure A4 . Highest ozone observed by the WP-3D aircraft and RHB research vessel during TexAQS 2006. The figure is in the same format as Figure A3. The dotted rectangle in the left panel indicates area covered in Figure A3.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 29 Finding A3 : Emissions from the Houston Ship Cha nnel play a major role in the formation of the highest 8-hour average ozone concentrat ions (and ozone design values) observed in the Houston area. Analysis: Sullivan et al.- U. Texas; Data: TCEQ. The general flow patterns that give rise to the highest 8-hour-aver age ozone concentrations in the HGB area have been investigated through back-traje ctory analysis. Monitoring sites to the south and west of downtown Houston have the highest oz one design values in the HGB area (defined as the 3-year average of the fourth-highest da ily maximum 8-hour-average ozone concentration). In Figure A5 the blue dots highlight the f our highest design values: 104 ppbv at Tom Bass CAMS 558, 103 ppbv at Bayland Park CAMS 53, 102 ppbv at West Houston CAMS 554, and 98 ppbv at Monroe CAMS 406. The green dot indicates th e Aldine site north of the city; as recently as 2002 Aldine had the maximum design value in the area, but it declined from 108 and 107 ppbv in 2001 and 2002, respectively, to 92 and 88 ppbv in 2005 and 2006, respectively. Relationship between Ozone Production Efficiency and the Slope of the O 3 versus NO y -NO x Correlation – A Cautionary Note Many investigations of atmospheric photochemi stry have interpreted the slope of the correlation of the measured O 3 concentration versus the m easured concentration of the oxidation products of NO x (i.e. the difference between measured concentrations of NO y and NO x ) as a direct measure of the ozone production efficiency (i.e. the number of O 3 molecules formed for each NO x oxidized.) The first paper that examined this correlation noted that nitric acid depositi on will affect the observed slope between O 3 and NO y -NO x correlations. If nitric acid is re moved from the atmosphere, then a measurement of NO y -NO x will underestimate the concentration of NO x oxidation products, and consequen tly, the slope of O 3 versus NO y -NO x will overestimate the ozone production efficiency. During TexAQS 2006, ozone production was studied under a variety of meteorological conditions. High wind speeds were found to enhance nitric acid loss, which caused the O 3 versus NO x oxidation products correlation slope to increase, often dramatica lly. The observed slopes varied from 2 to 16 in the coalesced plume from Hous ton, which was followed up to 170 km downwind. In many cases, the particularly high O 3 versus NO y -NO x slopes were primarily caused by particularly rapid loss of nitric acid from the transported plume, rather than from particularly high ozone production efficiency within the plume. Thus, when interpreting th e correlation slope of O 3 versus NO y -NO x as a measure of the ozone production efficiency, it is critical to consider the effect of nitric acid loss.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 30 Figure A5 . Harris County monitoring sites with the 4 highest 2006 design values marked in blue, and Aldine marked in green. In 2006, Bayland Park monitoring site (C53) had the area-wide highest regulatory design value of 104 ppbv. A set of near-surface back-trajectory ensembles was generated for 8-hour ozone exceedance days at Bayland Park and Monroe, the two regulatory sites with the hi ghest 2006 design values, and at Aldine to provide geographical contrast. Th e analysis covers the period from 2000 through 2006. On each day that a given monitor recorded an ozone exceedance, 7-hour back trajectories were calculated for all hours in which the measured ozone was above 85 ppbv. Hourly wind speed and direction data from 14 locations co vering Harris County provided the data for the back-trajectory calculati ons. Wind speeds were adjusted to account for varying anemometer exposure among sites using factors provided by Br yan Lambeth of TCEQ, and further increased by 20 percent to account for generally higher sp eeds above each monitor’s 10-meter anemometer tower. The data from all sites were pooled in an un-weighted average to provide one urban-scale wind speed and direction vector for each hour. Air parcel back trajectori es were calculated in 10-minute time steps beginning from the start time of each appropriate hour. The resulting trajectory ensembles include 79 da ys at Aldine, 104 at Bayland Park, and 56 at Monroe. Since exceedance days generally had multiple hours with ozone concentrations over 85 ppbv, the number of trajectories totale d 252 at Aldine, 281 at Bayland Park, and 110 at Monroe. One composite trajectory was produced from each ense mble by averaging the x-y coordinates of all trajectories in the ensemble as a function of transport time. The resultant composites are expected to be biased short, but based on maps of the ensembles a nd their centerlines, this effect is judged to be minimal. Figure A6 shows that each of these three compos ite back-trajectories r eaches back to the HSC area earlier in the day, with approximately 7-hour transport times to th e respective monitoringFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 31 sites. For Aldine the trajectories cluster to th e south-southwest, for Bayland Park they wrap around the south side of central Houston, and for Monroe they cl uster to the northeast. The consistency of the trajectories coming from th e HSC provides strong support for the conclusion that emissions from the petrochemical industrial f acilities in the HSC play a major role in the 8- hour ozone exceedances in the HGB area.

Figure A6 . Composite 7-hour near-surface back trajectori es for three Houston monitoring sites. All hours with O 3 ≥ 85 ppbv on 8-hour exceedance days for the 2000-2006 period were included in the composite analysis.

Finding A4 : Observations during TexAQS 2006 sh owed that nitryl chloride (ClNO 2 ) is formed within the nocturnal boundary layer when NO x emissions and marine influences are both present. Following sunrise, ClNO 2 photolyzes to yield chlorine atoms, which may lead to earlier and more rapid O 3 production in the Houston region. Analysis and Data: Roberts, Osthoff et al.-NOAA. The first reported atmospheric obser vations of nitryl chloride, ClNO 2 , were made aboard the Ronald H Brown during TexAQS 2006. Simulta neous measurements of ClNO 2 , N 2 O 5 , and aerosol size and com position, show that ClNO 2 is a general product formed when N 2 O 5 reacts on chloride-containing aerosol. N 2 O 5 is produced from nighttime reactions of NO 2 with O 3 . ClNO 2Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 32 is relatively stable at ni ght, but photolyzes upon sunris e to yield Cl atoms and NO 2 . These reactions link the nitrogen oxi des to halogen activation. An episode of high concentrations of ClNO 2 was observed on 2 September 2006 when the RHB was anchored in the Barbour’s Cut inlet located off Galveston Bay near HSC. Figure A7 shows the measured N 2 O 5 and ClNO 2 concentrations along with the Cl atom production rate calculated from the measured ClNO 2 and solar radiation flux. This Cl atom production reached almost 10 6

sec -1 , which is highly significant since Cl atoms react with VOC up to 100 times more rapidly than OH radicals. Figure A7 compares O 3 measured on 2 September with the average (±1 standard deviation) of the O 3 levels observed during the other days that the RHB was in the Houston-Galveston area during Tex AQS 2006. On 2 September the O 3 concentration increased more rapidly and reached a higher peak concentra tion compared to the average increase. This observation, while not definitive, is indicative of a potentially significant role for Cl atoms in O 3 production in polluted marine air.

A preliminary box model study was c onducted to investigate the ClNO 2 formation chemistry and to examine the effect of this Cl source on VOC-NO x photochemistry. The model consisted of the Master Chemical Mechanism (Jenkin et al ., 2003; Saunders et al., 2003), to which the N 2 O 5 aerosol chemistry and ClNO 2 photochemistry were added. The model results show that a reaction efficiency of 25% for N 2 O 5 on chloride-containing aerosol can account for the measurements in Figure A7. The Cl atoms produ ced upon sunrise led to a 2- to 3-fold increase in total peroxy radicals during the morning hours, which resulted in about a 15% higher O 3 concentration at the end of th e afternoon production period. These re sults indicate the need to add this Cl atom source to current regional photochemical models. Figure A7. N 2 O 5 and ClNO 2 measured on 2 September 2006, and calculated Cl atom production in the atmosphere (a); and the measured O 3 on 2 September, and the average from the othermeasurements in the HoustonGalveston area (b).Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 33 KEY CITATIONS AND INFORM ATION AND DATA SOURCES Jenkin, M.E., S.M. Saunders, V. Wagner, and M.J. Pilling. 2003. Protocol for the development of the master chemical mechanism MCMv3 (Par t B): Tropospheric degradation of aromatic volatile organic compounds. Atmospheric Chemistry and Physics, 3:181-193 (also Atmospheric Chemistry and Physic s Discussions, 2:1905-1938 (2002)). Ryerson, T.B., K.K. Perkins, M. Trainer, D.K. Nicks Jr., J.S. Holloway, J.A. Neuman, F. Flocke, A. Weinheimer, S.G. Donnelly, S. Schauffler, V. Stroud, E.L. Atlas, D.D. Parrish, R.W. Dissly, G.J. Frost, G. Hübler, R.O. Jakoubek, P. D. Goldan, W.C. Kuster, D.T. Sueper, A. Fried, B.P. Wert, R.J. Alvarez, R.M. Banta, L.S. Darby, C.J. Senff, and F.C. Fehsenfeld. 2006. Chemical and meteorological influences on extreme (>150 ppbv) ozone exceedances in the Houston metropolitan area. Draft Re port to TCEQ, Contract No. 582-4-65613. Saunders, S.M., M.E. Jenkin, R.G. Derwent, and M.J. Pilling. 2003. Protocol for the development of the master chemical mechan ism MCMv3 (Part A): Tropospheric degradation of non-aromatic volatile or ganic compounds. Atmospheric Chemistry and Physics, 3:161-180 (also Atmospheric Chemistry and Physics Discussions, 2:1847-1903 (2002)).Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 34 

### Response to Question B

QUESTION B How do the structure and dynamics of the planetary boundary layer and lower troposphere affect the ozone and aerosol conc entrations in Houston, Dallas, and East Texas?

BACKGROUND Meteorology in the boundary layer affects oz one levels through regul ation of near-source concentrations (wind speed, mixing height), b ackground levels (transport winds), photochemistry (solar radiation, temperature), and ai r parcel history (l ocal winds).

FINDINGS

Finding B1 : Boundary layer structure and mixing height near and over Galveston Bay and the eastern Houston ship channel area are spat ially complex and variable from day to day. Vertical mixing profiles often do not fit si mple models or conceptual profiles. High concentrations of pollutants are some times found above the boundary layer. Analysis: Nielsen-Gammon-Texas A&M; Senff, Darby, Banta, Angevine, Tucker, White-NOAA; Breitenbach, Dornblaser, Lambeth-TCEQ; Morri s-Valparaiso U.; Rappenglück, Perna-U. Houston. Data: Nielsen-Gammon-Texas A&M; Senff, Tucker, White, Darby, Angevine, BantaNOAA; Morris-Valparaiso U.; Rappenglück, Perna-U. Houston. Mixing depth (synonymous and used interchangeably he re with the term “mix ing height”) exerts an important control on the concentrations of pol lutants including ozone and its precursors. For example, when pollutants are released into a shallow mixed layer, hi gh concentrations of emissions can accumulate; this was observed using airborne ozone lidar during TexAQS 2000 (Banta et al., 2005). On the other hand, deep mixing can significantly dilute pollutants. Well away from the coastal region, the mixed la yer was relatively uniform over broad spatial areas. Well inland, peak after noon mixing heights were generally between 11⁄2 and 21⁄2 km, but could reach as high as 4 km. The mixed-layer hei ghts generally grew into late afternoon, often leveling off above 2 km. Over the Gulf of Me xico, shipboard lidar and platform radar windprofiler measurements indicated a relatively cons tant maritime mixed-layer depth of 600 m, with weak positive heat fluxes at th e surface, both day and night. The Houston urban area and major industrial sources are located in the coastal region, where themixing depth is highly variable in space and time, as determined by shipboard lidar and landbased sensors. Reasons for this variability incl ude land-sea contrast and the sea-breeze cycle, land-use differences, and along-shore coastal irre gularities, the major one in this area being Galveston Bay. The meteorology of the previous day and night is another influence on mixedlayer variability, but that effect is much more difficult to generalize. The coastal zone of southeast Texas is a transition region between the maritime boundary layer, with the relatively constant 600-m mixed-layer depths over the Gu lf of Mexico, and the deeper daytime mixed layers inland. The coastal influence on Houston mi xed-layer heights can be seen in their diurnal behavior. In contrast to the inland sites, where the mixed-laye r height generally increases until late afternoon, in the coastal zone the mixing height was observed to reach its maximum earlierFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 35 in the day (as early as late morning for sites ne ar the shore) and then to decrease, as the seabreeze front brought in cooler ma rine air through the afternoon.

Land-cover and land-use differen ces include the cross-region gradients in soil moisture and urban heat island processes. Climatologies and vegetation indicate moister conditions to the east of Houston and nearer to the coast, and drier co nditions to the west and inland. Airborne lidar flights revealed a significantly deeper mixed laye r over the arid regions to the west, consistent with mixing-depth data provide d by the profiler array, which indicated increases in peak afternoon mixing depths with distance from the coast.

Urban heat island. The issue of the urban heat island ( UHI) is also complicated in the coastal zone by the sea breeze. The e ffects of the sea breeze can be a ppreciated by contrasting Houston with an inland urban area, Dallas. Both Dalla s and Houston have comprehensive networks of surface meteorology and chemistry sensors. The sim ilarities of the networks and lack of terrain in Dallas and Houston allow for the comparison of their UHIs. The strength of the UHI is measured by taking the temperature difference from representative urban and rural sites. The Dallas UHI, unperturbed by thermal flows driven by land/sea temperatur e differences, was a well-defined phenomenon during the summers of 2000-2006 (Fig. B1a). Including all weather conditions, the average nighttime T urban – T rural temperature difference was between 1.5o and 2.0o C and the average daytime difference was ~ 1.0o C. Analysis of Houston temperature data, however, revealed a different picture due to th e Bay and Gulf breezes (Fig. B1b). Although the Houston UHI was a distinct phe nomenon, even when including al l weather conditions, the Bay or Gulf breezes modified the Houston UHI by cooling the city. Average nighttime T urban – T rural temperature differences in Houston were between 1.75o and 2.75o C. Ho wever, during the day, the rural areas to the north and west of the city were often warmer than the downtown area during afternoon hours as a re sult of the sea breeze. Averaging the Houston T urban – T rural temperature differences over the summers of 2000-2006 indicated a very small urban-rural temperature difference between 1400 and 1600 local st andard time (LST), in contrast to Dallas, which had a T urban – T rural temperature difference of ~1° C. In some individual years, such as 2000, 2003, 2005 and 2006, the Houston urban areas were ac tually cooler than the rural areas, on average, in the mid-afternoon. These years had more Bay-breeze/Gulf-breeze activity to cool the urban area.

Figure B1. T urban – T rural for each of the 7 summers analyzed , and the average for all summers. “Summer” includes all days from 1 June– 30 Se ptember. a) Dallas. b) Houston.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 36 The effects of along-shore irregularity combined w ith the other coastal eff ects can be seen in the distributions of daytime mixed-layer heights. Midday mixed-layer heights measured at an inland site at Moody, Texas (near Waco) were relatively consistent from day to day, thus exhibiting a rather narrow distribution with a mean of ~1.5 km. Near the shor e of Galveston Bay at LaPorte, a much broader distribution of mixed-layer he ights, including high frequencies below 1 km, indicates the much more complex influences on mixing depth in the coastal zone. Spatial variability of mixing depth was evident in airborne lidar flights, with shallow mixing layers over Galveston Bay and deeper mixing layer depths overthe Houston urban area as a result of heatisland effects. Variations in mixing depth at ne arby locations often reflected advection of high or low mixing layer depths downwind of the urba n area or Galveston Bay, for example. Sounding observations from the University of H ouston (UH) campus (e.g., Fig. B2) and from the Ronald H. Brown (when stationed in Galveston Bay) of ten showed a very complex vertical ozone structure, with no well-marked transiti on between the local bounda ry layer and the air above; this was consistent with shipboard Differential Absorp tion Lidar (DIAL) and Doppler lidar profiles. This complexity is likely to be underrepresented in numerical models.

Figure B2. Ozonesonde profiles of ozone (left, 0-100 p pb) and temperature (right, 270-300 K) from the surface to 6,000 m above ground level (AGL), for a 1200 UTC ascent on 31 August 2006, released from the University of Houston campus. The minimum in the ozone profile near 1,000 m AGL was associated with a layer of high relative humidity and clouds. Spatial variability of mixing heig ht during TexAQS 2000 and TexAQS 2006. Multi-sensor spatial analyses of mixing heights, using data from the TexAQS field campaigns, illustrate the effects of these diverse influences, often showing mixing hei ght varying by a factor of two or more acrossFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 37 Houston. An example of large spatial variabilit y in mixing heights is shown in Figure B3. A combination of fixed-location ground-based sensors and mobile airborne sensors is essential for determining the spatial and temporal mixing he ight variability across the Houston metropolitan

area.

Figure B3. Mixing height measurements in early afternoon in Houston, 1 September 2000. Colors indicate observed or estimated mixing heights. Shown are mixing heights from airborne instruments (colored lines) and mixing heights from profilers , soundings, and aircraft ascents and descents (circles). Note the large spatial variability north a nd east of Houston (Nielsen-Gammon et al., 2007).

Mixing height and O 3 concentrations. Attempts to determine general relationships between daytime mixing heights and ozone concentrations have been mostly fruitless. The expected relationship is that higher concen trations would be associated w ith lower mixed-layer heights. Northerly or northeasterly large-scale flow, however, is often associated with higher concentrations from distant continental sources , and continental air masses often have higher mixing heights. Conversely, and also opposing th e expected mixing-height/ozone relationship, out over Galveston Bay, large-scal e southerly flow off the Gulf of Mexico was observed to be associated with more clean and moist air but sh allower mixed layers. On the other hand, when pollutants are emitted into the shallow mixed layer of Galveston Bay and carried over the Bay, pollutant concentrations have been shown to rema in very high – in agreement with expectation. In another scenario, aircraft sa mpling of urban or other plumes some distance downwind of the sources finds well mixed regions outside and inside of the plum es where the ozone is low and high, respectively, on the same day, but the mixi ng heights are essentiall y the same, yielding noFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 38 systematic relationship. All of these effect s confound the search for general relationships, producing seemingly random or otherwis e meaningless scatter plots of O 3 and mixing heights Nighttime mixing. Nighttime boundary layer st ructure in the Houston-Galveston area is less easy to characterize. UH tethersonde and ozonesonde data indicate th at nighttime depletion can occur at elevations as high as 200 m, implying that the top of the urban boundary layer tends to be ~200 m or less at night. Thus, nighttime mixing and transport e ffects in the Houston-Galveston area remain an area of longer-term research needs. Layering of pollutants. O 3 and aerosol pollutants in the H ouston-Galveston area were often confined to the mixed layer (Fig. B4a,b). But occasionally significan t concentrations of pollutants were found above the mixed-layer height (Fig. B4c,d). Pollutants, especially aerosol, sometimes exhibited a complex layered structur e, in which the origin of pollution in the individual layers was difficult to determine (Fi g. B2). In the analysis performed to date, high concentrations of pollutants found in deep layers (more than a few hundred meters thick) above the mixed layer (Fig. B4d) were found to originat e in distant regions outside of Texas to the northeast or east.

Figure B4. Airborne lidar time-height cross sections of aerosol backscatter (top, in units of 10 -8 m -1

sr -1 ) and ozone concentrations (bottom, in ppb) for a), b) 30 August 2006, when high ozone and aerosol concentrations were confined to the mi xed layer, and c), d) 4 September 2006, when the mixed layer was indicated by higher aerosol c oncentrations, but the high ozone was above the boundary layer.

Finding B2 : Complex coastal winds, resulting from weak larger-scale winds over the area, occurred during many, but not all, ozone exceedance days in the Houston-GalvestonBrazoria nonattainment area. Almost no high-ozone days during TexAQS 2000 resembled any high ozone days from TexAQS 2006, but collectively the 2000 and 2006 field intensives sampled the full range of mete orological conditions associated with high ozone events. Analysis: Banta-NOAA; Nielsen-Gammon-Texa s A&M; Bryan Lambeth-TCEQ; Gary MorrisValparaiso U.; Perna, Rappenglück-U. Houston; Senff, Darby, Angevine-NOAA; Breitenbach, Dornblaser-TCEQ. Data: Nielsen-Gammon-Te xas A&M; Senff, Tucker, White, Darby, Angevine, Banta-NOAA; Morris- Valparaiso U.; Rappenglück, Perna-U. Houston. High ozone concentrations were most often asso ciated with light winds and daytime sea-breeze reversals (often leading to st agnation occurrences; (D arby, 2005]), but some days with strongerFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 39 winds also had O 3 exceedances during both 2000 and 2006. The interplay between local wind patterns and large-scale transport produced a wide variety of meteorologi cal scenarios for ozone events in the Houston area. Broadly speaking, when mean winds (here measured using 24-hrmean resultant winds from nearshore buoy 42035) were light, recircula tion was likely, whereas when mean winds were strong, the wind direction tended to be steady throughout the day. The direction of the mean wind determines when st agnation will occur if wi nds are light enough, and also determines the direction of tr ansport of the Houston ozone plume. By plotting the west-east and south-north compone nts of the mean winds on a scatter diagram, the range of meteorological conditions during an ozone episode or an extended period may be determined. Such a diagram is shown in Fig. B5 for the late summer ozone season in Houston. During TexAQS 2000, most high-ozone events o ccurred when the mean wind was from the southeast (regime 1) or southwest (regime 2), but no light northeast wind even ts occurred at all. In contrast, during TexAQS 2006, wind conditions were much more representative of climatology, and several high-ozone events occurred under light nor theast winds. Those days in which the wind was light from the southeast dur ing 2006 tended to feature widespread clouds and precipitation. Thus, almost no high-ozone days during TexAQS 2000 resembled any high ozone days from TexAQS 2006, but collectively the 2000 and 2006 field intensives sampled the full range of meteorological conditions associated with high ozone events.

Figure B5

. Scatter diagram of daily mean winds at buoy 42035, near Houston, during the period 1 August – 15 October, 1998-2006. Grid lines are every 5 m/s; a dot in the upper left quadrant implies winds from the southeast. The wind regimes most conducive to high 1-hr ozone in the Houston area are circled. Days during TexAQS 2000 are indicated with pink squares, and days during TexAQS 2006 are indicated with yellow triangles; blue dots represent data from all other years. Peak ozone and wind speed. The overall relation ship between peak O 3 concentrations and wind speed is shown in Fig. B6, which indicates th e expected strong nega tive correlation betweenFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 40 maximum in-network ozone enhancements (or “add-on”) above background and wind speed in the Houston area (see red data and line on Fig. B6 ). The 10-hr trajectory displacement is a surrogate for mean wind speed. Stronger winds (larger displacements) produce greater dilution of the pollution emissions, and thus lower concen trations of pollutants. Analysis of groundbased measurements over several years indicates a threshold effect in Houston (NielsenGammon et al., 2005). Large loca l add-ons are typically presen t when large-scale winds are weaker than about 3 m s -1 , light enough to allow recirculati on, and the add-on decreases rapidly for wind speeds larger than 3 m s -1 . This effect is evident especia lly in the airborne data in Fig. B6. A steady 3 m s -1 wind would produce a 10-hr displaceme nt of 108 km, and below this value is where peak O 3 add-on concentrations become very large.

R

2 = 0.5033

R

2 = 0.3902

-20

0 20 40 60 80 100 120 140 160 180 200

0 100 200 300 400

10-hr displacement (km)O3 add on Airb add on

Sfc add on Linear (Airb add on) Linear (Sfc add on)

Figure B6. Houston ozone enhancement (peak ozon e values in urban plume minus background values) plotted vs. displacement of 10-hr traject ories, representing the vector-mean wind for the period, starting at Houston at 8:00 a.m. CST. Red symbols indicate data from surface measurement network (1-hr average), and blue symbols indicate data from airborne ozone lidar and WP-3D. “Add on” represents peak ozone concentrations observe d minus background concentrations. Lines are linear best-fit lines.

The blue points in Fig. B6 show the airborne-lid ar-determined peak ozone concentrations vs. 10- hr trajectory displacements. The concentrations again decrease with increasing speed, but are everywhere larger than the surface measurements. Reasons for this discrepancy include that the airborne data represent conditions aloft not subject to surface proce sses such as removal, that the airborne lidar data represent av eraging over shorter intervals, and that on days with stronger winds, plumes are narrower and harder to sample by fixed networks and al so high concentrations may be blown out of the surface network before being sampled. The last issue was expected to be a greater s ource of discrepancy be tween airborne and groundnetwork sampling, because the aircraft samples the pollution plume wherever it is, even though the maximum values may be occurring beyond the surface measurement network before the photochemical reactions are complete. Indeed, indi vidual days have been identified with large discrepancies. However, Fig. B6 shows that, in general, light winds (small 10-hr displacements)Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 41 were associated with large O 3 enhancements by the Houston area, and stronger wind speeds, with smaller enhancements. Several cases of stronger- wind days have been identified when the total O 3 concentrations (background plus add-on) exceeded the 1-hr sta ndard. Those days, most often associated with high background values, are bein g studied further to determine all processes responsible for the high concentrations. On such stronger-wind days with high backgroun d levels, high pollutant concentrations were generally being advected into th e Houston area under easterly or northerly continental flow. Airborne ozone lidar flights sampling air masses entering Texas from the east on three different days found concentrations of 50, 80, and 90 ppb in the inflow air. Another t ypical scenario is for post-cold-frontal northerly flow to be associated with high pollu tant concentrations. When the offshore winds are weak enough, sea-breeze stagnation occurs, and when they are stronger, they are associated with high background. During 2006 all ozone episodes in the month of September occurred in a post-frontal environment. Fo r example, a four-day sequence of ozonesonde profiles (Fig. B7) showed an in crease in background ozone with in the free troposphere up to 5000 m AGL, corresponding to transport from the c ontinental United States after the passage of the front.

Figure B7. Ozonesonde profiles at ~1200 CST on 28, 29, 30, and 31 August 2006. Passage of cold front occurred on 29 August 2006. Before the passag e of the front, marine air masses with O 3 concentrations of 30 ppb had c oncentrations even lower than the EPA boundary-condition benchmark values (40 ppb, red dotted vertical line). In sub sequent days, background ozone increased throughout the troposphere up to 5000 m AGL due to the shif t in wind direction after the frontal passage. Many of these episodes were associated with st rong elevated inversions. Higher afternoon ozone peaks occurred where there was an extremely dry air mass aloft, although it was not always associated with the strongest inversion observed. The air masses aloft with the strongest windsFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 42 (greater than 8 m s -1 ) and driest air did not have extremel y high ozone values associated with them, mainly because these episodes occurred ri ght after a frontal passage where the northerly flow was strong enough to keep ozone peaks in check. Thus, the Houston-Galveston-Br azoria area produces a treme ndous amount of ozone. Even under relatively strong wind conditions, the result can be high pollutant concentrations, although the very highest concentrations are produced on days with weak-wind or sea-breeze-reversal conditions. Even with the 8-hr standard, most, but not all, 8-hr exceedances during the TexAQS II field intensive (and other 2005-6 episodes) oc curred when winds were light enough to allow stagnation/recirculation.

Finding B3 : After sea breeze days, the Houston plume was broadly dispersed at night through the formation of low-level jets. Analysis: Nielsen-Gammon-Texas A&M; Banta, Se nff, Ryerson, Darby, Tucker, White-NOAA; Lambeth-TCEQ. Data: Nielsen-Gammon-Texas A&M; Senff, Tucker, White, Darby, AngevineNOAA. Trajectory analysis of nighttime transport of the Houston plume, based on the wind profiler network, indicates that the Houston plume sometimes remains a coherent entity, subject to little wind shear (see Fig. B8a,b), but at other times is dispersed over a broad portion of Texas by strongly sheared winds (Fig. B8c ,d). Broad dispersal is favored after sea breeze days when nighttime decoupling allows a strong low-level je t to form from the remains of the sea breeze circulation. The wind speed is strongest at 300-500 m, decreasing above and below.

Figure B8. Overnight trajectories for four elevations above ground level, based on hourly radar wind profiler measurements, for the nights of a) 16- 17 August, b) 14-15 August, c) 31 August-1 September, and d) 1-2 September. Red trajectori es were averaged over the vertical interval from 200 to 500 m;, black, from 500-800 m; green, from 800-1200 m;, and blue, from 1200-1800 m. Lower trajectories or those ending up to the north of their origin are not likely to be contaminated by signal from migrating birds.

An inland surge of winds found in the 200-600 m la yer at night has been called the sea-breeze low-level jet. The essential characteristics of th is jet are shown in a sequence of images from an MM5 model forecast from 8 June 2006 (Fig. B9). In the first panel, at 1500 local standard time (LST), the winds are light and onshore. This wind pattern is particul arly conducive to the formation of a sea-breeze low-level jet. Al ong the coast, a sea br eeze has developed, and onshore wind speeds are locally 4-7 m s -1 . Around sunset, in the s econd panel, the sea breeze wind maximum has moved inland and has increased in speed to over 9 m s -1 in places. The LLJ occurs in a broad area along the coast, rather than a narrow along-wind ba nd. In the lower left panel, at 2100 LST, the sea-breeze low-level jet is about 200 km inland and has continued toFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 43 increase in intensity, with peak wind speeds over 12 m s -1 . The leading edge of the jet is very sharp, implying a sudden increase in wind speed, whereas along the trailing edge the decline in wind speed is gradual. Note also that the wind direction, initially south easterly, has veered to southerly under the influence of the Coriolis forc e. This veering continues through the night, and the wind direction is southwesterly by 0000 LST (fin al panel). By this time, with the winds no longer blowing toward lower pressure, the wind sp eed within the jet has begun to decrease. A similar wind evolution is simulated whenever winds are light and ons hore during the day and precipitation is sparse or absent. Sea-breeze lo w-level jet passages are also evident in wind profiler observations from 2005-6.

Figure B9. Forecasted 300 m winds (indicated with barb s and isotach shading) and pressure (black isobars), 1500 LST 8 June 2006 (upp er left), 1800 LST (upper right), 2100 LST (lower left) and 0000 LST, 9 June (lower right). The sea-breeze low-leve l jet is the band of strong wind speeds originating along the coast at 1500 CST and accelera ting inland through the evening. A dramatic example of nocturnal transport has been documented for 8 September 2006, when high daytime concentrations of ozone that ha d accumulated over Houston during the previous day traveled to Dallas ov ernight, contributing to enhanced con centrations in Dallas the next day.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 44 This episode is discussed in more detail in th e Response to Question H. Other examples of overnight transport of the Houst on plume were identified in data from the instrumented tower at Moody, Texas (near Waco) and in the Tyler-Longview areas of northeast Texas. In a further example from 1 September, the Houston plume m oved to the south of th e city overnight, and then was brought back in the sea bree ze the next day to produce enhanced O 3 concentrations in the western suburbs. Calculations of the O 3 flux from Houston indi cate that if these concentrations were spread over an area 100 by 100 miles by 2 km de ep, it would raise the concentrations by 10 ppb over the entire volume. Th ese calculations are furt her discussed in the Response to Question G.

Finding B4 : The Dallas ozone plume can extend well beyond the monitoring network. Analysis: Senff, Darby, Banta-NOAA; Breitenbac h, Lambeth-TCEQ. Data: Senff, Darby, BantaNOAA; Lambeth-TCEQ.
An airborne O 3 lidar flight, sampling the DFW plume on 13 September in northerly flow of ~ 5

m s -1 , found peak O 3 values of 90-95 ppb against a b ackground of ~ 65 ppb (Fig. B10), representing an enhancement of 25-30 ppb, in agr eement with previous estimates. These high O 3 values were observed in a dist inct urban plume extending past the southernmost cross-wind flight leg at a distance of 85 km downwind of DFW, indicating that the plume extended much farther downwind than this. It seems likely that the highest ozone occurs beyond the margin of the monitoring network on such occasions, as wa s previously noted for the Houston network. Under southerly large-scale flow, recently installed ozone sensor s in southern Oklahoma also indicated elevated ozone concentr ations resulting from the DFW plume, at a distance of at least 240 km (near Lawton, Oklahoma).

Figure B10. a) Flight track of the Twin Otter ai rborne ozone lidar, s howing color-coded ozone concentrations averaged between 500 and 10 00 m MSL (ozone scale from 50 to 100 ppb), for 13 September, a day with stiff north-northeasterly flow . b) Vertical time-height cross section of ozone [ppb, same scale as in a), vertical scale from 0 to 1800 m MSL, horizontal scale from 2145 to 2230 UTC] for southernmost cross-wind leg, showi ng plume of higher ozone from Dallas-Ft. Worth.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 45 KEY CITATIONS AND INFORM ATION AND DATA SOURCES Banta, R.M., C.J. Senff, T.B. Ryerson, J. Ni elsen-Gammon, L.S. Darby, R.J. Alvarez, S.P. Sandberg, E.J. Williams, and M. Trainer. 2005. A bad air day in Houston. Bull. Amer. Meteor. Soc., 86:657-669. Darby, L.S. 2005. Cluster analysis of surface wind s in Houston, Texas and the impact of wind patterns on ozone. J. Appl. Meteor

. 44:1788-1806. Nielsen-Gammon, J.W., J. Tobi n, and A. McNeel. 2005. A Conceptual Model for Eight-Hour Ozone Exceedances in Houston, Texas. Part II: Eight-Hour Ozone Exceedances in the Houston-Galveston Metropolitan Ar ea. HARC/TERC/TCEQ Report, 79 pp. http://www.harc.edu/Projects/AirQ uality/Projects/Projects/H012.2004.8HRA Nielsen-Gammon, J.W., C.L. Powe ll, M.J. Mahoney, W.M. Angevine, C. Senff, A. White, C. Berkowitz, C. Doran, and K. Knupp. 2007. Multis ensor estimation of mixing heights over a coastal city. J. Appl. Meteor. Clim., in press.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 46 

### Response to Question C

QUESTION C Are highly reactive VOC and NO x emissions and resulting ambi ent concentrations still at the same levels in Houston as they were in 2000? How have they changed spatially and temp orally? Are there sp ecific locations where particularly large quantities of HRVOC are still being emitted? Are those emissions continuous or episodic? How well do the reported emissions inventorie s explain the observed concentrations of VOC and NO x ?

BACKGROUND Questions C, D, and E all deal with emissions. Here, Question C specifically addresses highly reactive VOC and co-located NO x emissions in the Houston area. Question D addresses all other ozone and aerosol precursor emissions, biogenic as well as anthropogenic, that are included in emission inventories. Question E addresses evid ence for additional, unrecognized sources of precursor emissions.

FINDINGS

Finding C1 : There are indications that ethene (the lightest HRVOC) emissions from industrial sources in the Houston area decreased by 40 (±20)%, i.e., by a factor of between 1.25 and 2.5, between 2000 and 2006.

Comparison of ethene/NO x emission ratios measured du ring TexAQS 2000, TexAQS 2006, and one 2002 aircraft flight Analysis: de Gouw et al.-NOAA. Data: de Gouw , Ryerson et al.-NOAA; Atlas et al.–U. Miami The emissions of ethene from numerous point so urces were individually characterized using the Electra and WP-3D aircraft measurements made in TexAQS 2000, TexAQS 2006, and on one flight in the southeast Texas re gion in April 2002. Determinati on of ethene/NOx emission ratios was most straightforward for the isolated petroc hemical plants to the south of Houston. These facilities were extensively investigated in 2000 and 2006, and an additional research flight was made in 2002 when the WP-3D transited to a stu dy in California. Figur e C1 shows the flight track in 2002 (Ryerson et al., 2003 ) and an example of a flight made in 2006, along with results of the NO x and ethene measurements. Apart from Texa s City, there appear to be changes in the ethene/NO x emission ratios. The results are summarized in Table C1, and show decreases in the ratio by a factor of 3-7 for Sweeny, Freeport, and Chocolate Bayou. Further research is necessary to determine if the lower emission ra tios were systematic, or only happened to be lower on the 29 September flight; preliminary an alyses have shown that emission ratios are variable within factors of 2-3 between different flights. In additi on, the high-time-resolution ethene data from the WP-3D have indicated that ethene and NO x enhancements are not always well correlated. Evidently the sources of thes e species are sometimes not co-located, making it difficult to define emission ratios in those cases.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 47 Figure C1 . Measurements of NO x and ethene downwind from 4 isolat ed petrochemical plants to the south of Houston. The top panels show the flight tracks of the WP-3D on 22 April 2002, (Ryerson et al., 2003) and on 29 September 2006. The lower pane ls show the measurement results plotted as a function of longitude.

Table C1 . Ethene/NO x emission ratios for petrochemical complexes determined from measurements in 2000, 2002, and 2006, compared with emission inventories.

Ethene/NO x Emission Ratios Inventories Measured 1999 a 2004 b 2000 c 2002 c 2006 Sweeny 0.05 0.019 3.6 1.7 0.5 Freeport 0.05 0.030 1.5 0.62 0.32 Choc. Bayou 0.08 0.048 2.0 1.2 0.62

a TNRCC emission inventory.
b TCEQ point source emission inventory with 1999 VOC speciation.

c Ryerson et al. (2003.)Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 48 Comparison of ethene and formaldehyde co ncentration distributions during TexAQS 2000 and 2006 Analysis: de Gouw et al.-NOAA. Data: de Gouw , Ryerson, Holloway, et al.-NOAA; Atlas et al.– U. Miami; Fried et al.-NCAR. Figure C2 compares the 2000 a nd 2006 airborne ethene measur ements made below 1000 m altitude inside a box around Hous ton (Figure C2, panel A). The median ethene from the whole air samples (WAS) was 40% lower in 2006 than in 2000 (Figure C2, panel B). The median ethene from the laser photo-ac oustic spectroscopy (LPAS) measurements was even lower; one can show that the targeted filling of WAS inside pollution plumes leads to a bias in the WAS median. A similar comparison of the 2006 data from the Ronald H. Brown while in Barbour’s Cut with the 2000 data from La Porte airport (t hese two locations are in close proximity) indicates a similar 40% decr ease (not shown here).

Figure C2. Cumulative probability diagrams for ethene, wind speed, and temperature measured from the NOAA WP-3D inside the box around Houston shown in panel A and below 1000 m altitude.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 49 The question arises whether the differences between 2000 and 2006 are due to a decrease in HRVOC emissions or to a difference in meteor ology. Average wind speeds in 2006 were higher than in 2000 (Figure C2, panel C), which would l ead to a greater dilution of ethene emissions and lower mixing ratios in 2006. However, averag e temperatures in 2006 were lower than in 2000 (Figure C2, panel D). Higher temperatures can be associated with deeper boundary layers and thus a larger volume within which the emi ssions are mixed and diluted, which would imply higher mixing ratios in 2006. All emissions are subject to the same mete orology. Comparison of other emitted species between 2000 and 2006 can provide additional insight into possibl e meteorological differences. If the decrease in ethene between 2000 and 2006 were due to meteorology, then other trace gases should exhibit similar trends. If, on the other hand, the decrease were due to a decrease in emissions, then other trace gases would show ambient con centration trends characteristic of their own emission trends. Figure C3 shows the diffe rence in median mixing ratio between 2000 and 2006 for six species. On-road mobile emissions do minate the emissions of ethyne and CO; their mixing ratios showed only small changes betw een 2000 and 2006. (The CO mixing ratios were corrected for the observed backgr ound mixing ratios.) A decrease in CO is expected because of the modernization of the vehicle fleet between 2000 and 2006; the observed decrease of ~3% per year is slightly smaller than the reported histor ical trend of 4.6% per ye ar (Parrish, 2006). NO y showed a significant decrease betw een 2000 and 2006; this is attri buted to the installation of emissions reduction technologies in pow er plants and other industrial NO x point sources (see Finding C3 below). SO 2 increased slightly between 2000 and 2006; reductions were not expected as emissions c ontrols have focused on NO x . The largest decreases were observed in the mixing ratios of ethene and formaldehyde (H CHO), its main photoproduct. From this comparison we conclude that the meteorological differences had compen sating effects on the two years, and that the decrease in ethene betw een 2000 and 2006 was due to a reduction in its industrial emissions.

Figure C3. Difference in median mixing ratio for several trace gases measured from the WP-3D inside a box around Houston and below 1000 m altitude.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 50 Long-term data sets Analysis: Estes et al.-TCEQ; Data: TCEQ.
In the Houston area there have been extensive VOC measurements made by as many as eight auto-GC systems and by canister-based methods. Measurements were begun at some sites as early as 1997, giving temporal coverage over on e decade by the end of 2006. Figure C4 presents results from two sites near the Ship Channel. The median ambient ethene levels at both sites decreased by about a factor of two between 1997 and 2005. Similar decreases were also seen for propene (not shown). Analysis by Sather and Ca vender (2007) also show s significant decreases of ambient concentrations of ethene and prope ne, in qualitative agreem ent with Figure C4.

Figure C4 . Results of ethene measurements by auto-GCs at two sites near the Houston Ship Channel: 9 years of data from Clinton (on the western end) a nd 8 years of data from Deer Park (on the eastern end).

Summary of evidence
Emission ratios of et hene relative to NO x from several isolated petrochemical plants in 2006 were lower in comparison with results from 2000 and 2002. This decrease in ratios must be due to a decrease in ethene emissions, since the NO x emissions have also decreased in many cases (see Finding G3). Further, lower mixing ratios of ethene and formaldehyde were observed in 2006 in comparison with 2000, and this change ca nnot be explained by differences in dilution rates between the two years. Thus, analyses based on four differe nt measured parameters (ethene/NO x emission ratios in plumes of petrochemical facilities, the ambient di stribution of ethene con centrations, the ambient distribution of formaldehyde, and long-term au to-GC ethene measurements) have all found evidence for a significant decrease in ethene emi ssions. The unanimity of these four analysesFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 51 increases our confidence that a significant d ecrease in HRVOC emissions from Houston area petrochemical facilities has act ually occurred in the period between 2000 and 2006. Considering all of these analyses, the best estimate is that ethene emissions have decreased by about 40% (i.e., a factor of 1.7) between 2000 and 2006. Th e much more limited ev idence available for propene suggests that emissions of this HRVOC may have decreased similarly. It is important but difficult to a ssign confidence limits to the magnit ude of the decrease. Our best estimate is that the decrease is 40 ± 20%, whic h corresponds to a decrease by a factor of 1.25 to 2.5. We consider it unlikely, but cannot categori cally exclude the possibi lity that there has in fact been no significant decrease.

Finding C2 : Measurements of ethene emission flux es from petrochemical facilities during TexAQS 2006 indicate that the 2004 TCEQ po int source database underestimates these 2006 emissions by one to two orders of magnitude. Repeated sampling of the same petrochemical facility showed that the ethene emission flux remained constant to within a factor of two. Analysis and data: de Gouw et al.- NOAA; Mellqvist et al.–Chalmers U.
Emissions of ethene were determined duri ng TexAQS 2006 by two completely independent methods. First, column measurements of ethe ne by absorption of solar IR radiation were obtained from the solar occultation flux (S OF) mobile laboratory operated by Chalmers University (Gothenburg, Sweden). In this method, the ethene emission flux was estimated by combining the column data with measured vertic al wind profiles and then integrating across the width of the plume in close proximity to the emission source. Second, ethene was measured onboard the WP-3D aircraft using a new laser ph oto-acoustic spectroscopy (LPAS) instrument. An example of the resulting data is shown from 25 September when the WP-3D and the SOF van both sampled the emissions from the Mont Belvieu complex (Figure C5).

Figure C5. Transect of the WP-3D just downwind from th e Mont Belvieu complex to the northeast of HSC, color-coded by ethene measured by LPAS. Ethene sources from the 2004 TCEQ point source database are indicated by the blue triangles, with the size proportional to the source strength. The measured time series of ethene for this transect is shown on the right.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 52 The ethene flux was estimated from the aircraft measurements by integrating across the width of the plume and by assuming that the plume was hom ogeneously distributed over the height of the boundary layer. A flux of 280 kg h -1 is the result for this exampl e. The result from the SOF van from the same complex on the same day was 500 kg h -1 , i.e., agreement within a factor of 2. Flux estimates from the SOF and WP-3D measurem ents were compared for different sources (Sweeny, Freeport, Texas City) a nd generally agreed w ithin a factor of 2, with no systematic difference as to which tec hnique was higher or lower.

The WP-3D determined the flux from Mont Belvie u on a total of 10 downwind transects. Figure C6 shows a histogram of the WP-3D results (gre y bars); the average ca lculated flux is 470 kg h -1 with a standard deviation of 160 kg h -1 . Two considerations can se rve to put these fluxes in perspective. First, the 2004 TC EQ point source database estima ted the ethene emissions from Mont Belvieu to be 29 kg h -1 , a factor of 10-40 lower than th e values measured during TexAQS 2006. Second, Murphy and Allen (2005) investigated th e role of large, accidental releases of HRVOC in ozone formation in the HGB area. Th ey identified 763 HRVOC emission events in a one-year period (31 January 2003 to 30 January 2004). More than half of these events released less than 1000 lbs (454 kg) total HRVOC. Thus, the Mont Belvie u complex, as an example of petrochemical facilities in the HGB area, routin ely emits more ethene each hour than the total released in most of the indivi dual accidental release events c onsidered by Murphy and Allen. These facilities represent a significantly la rger source of HRVOC and more substantial contribution to ozone formation than indi cated by current emission inventories.

Figure C6. Histogram of ethene fluxes determined from aircraft measurements of 10 transects. Also indicated are the SOF result from September 25, the average SOF result from 3 days of measurements, and the 2004 TCEQ point source database.

Finding C3 : Close to petrochemical HRVOC sources, the OH reactivity of propene is generally greater than that of ethene. Analysis: de Gouw et al.-NOAA; Data: Atlas et al.-U. Miami. Close to emission sources, the re activity of propene ge nerally outweighs that of ethene. To illustrate, Figure C7 shows the OH reactivity of propene (the product of the propene concentration and the rate coefficient for its reacti on with OH) versus that of ethene from all theFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 53 whole air sample measurements onboard the WP-3D. At high reactivity (>0.5 s -1 ) the data points are well above the blue line that indicates e qual reactivity of ethene and propene. At lower reactivity (<0.1 s -1 ), the reactivity of ethene is higher than that of pr opene; the lifetime of ethene is longer by about a factor of 3, and therefore it persists furthe r downwind from sources. Thus, VOC measurements made at routin e monitoring sites, generally located relatively distant from the facilities, will not accurately reflect the propene OH reactivity contributions unless the relative lifetimes of the HRVOC are carefully considered.

Figure C7. OH reactivity of propene versus that of ethene from all the data from the WP-3D. The blue line indicates equal reactivity of ethene and propene; the gray area shows the range where the reactivities of ethene and propene differ by no mo re than a factor of 3, higher or lower.

Finding C4 : The latest available emission inventor ies underestimate ethene emissions by approximately an order of magnitude. Analysis: Jolly et al.-TCEQ; Data: TCEQ. The TexAQS 2000 study established that inventories underestimat ed emission fluxes of HRVOC from petrochemical facilities by one to two orders of magnitude (Ryerson et al., 2003.) A remarkable feature of Table C1 and Figure C6 is that appreciation of th is finding has not been reflected in the inventory evol ution. For example, total HRVO C emissions included in the Harris County Point Source EI for 2000-2004 were fairly steady across th ose years, with the lowest year (2002, at 3300 tons) being about 83 percent of the hi ghest year (2004, 4000 tons). Total VOCs in the county in the 2000-2005 peri od differed approximately 13 percent between the lowest year (2003, ~29,000 tons) and the high est year (2000, ~33,500 tons). Consequently, the latest available emission inventories still underestimate HRVOC emissions (as judged by the ethene comparisons summarized in Table C1 and Figure C6) by at least an order of magnitude. In an effort to improve this situation, TC EQ collected the special 2006 Hourly Emission Inventory during the TexAQS 2006 intensive fr om 15 August through 15 September. During this period, 141 sites in eas tern Texas reported their hourly emissions of VOC, NO x , CO, and SO 2 from predetermined industrial sources, wh ich were selected based upon the following criteria: sources subject to HRVOC rules, NO x and sulfur dioxide sour ces equipped with CEMS,Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 54 and emissions sources located near ambient ai r monitoring sites. For HRVOC the reported emissions were based upon process flow monitori ng (flares, cooling towers) that was required beginning in January 2006. This inventory wa s the first in HGB conducted since compliance deadlines passed for key HRVO C regulations promulgated by the TCEQ. These hourly estimates were made only for a subset of pro cess units and emission poi nts within the reporting plants, and only a subset of plants that normally report in the HGB EI did so in the Hourly EI. Both of these factors add uncertainty to how we ll this inventory represents the total HGB VOC inventory. In HGB, about 10-11% of the process units and emission points that emit ethene and propene in the annual (periodic) emission inventory were included in the Hourly EI. However, these units accounted for 23-24% of all ethene/pr opene emissions reported in the annual EI.

0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 500

, 000

Hourly EI OSD EI Hourly EI OSD EI Hourly EI OSD EI Hourly EI OSD EI

Cooling Towers Flares Equipment Leak Fugitives Unknown source typeLb per 32 days Propylene

Ethylene

Butenes

_1_3__Butadiene

The results from the hourly EI suggests that reported emissions of total VOC and HRVOC species may increase in the coming years. Figure C8 shows emissions changes in inventoried emissions from cooling towers and flares, as well as for equipment leak fugitives (which were poorly represented in the hourly EI). The to tal HRVOC emissions from inventoried flares, increased about 2.4 fold (95 tons to 231 tons), although the much smaller emissions from cooling towers decreased by 28% (9.9 to 7.7 tons in the 32 da y period). Overall, the total reported ethene and propene emissions in HGB approximately doubled. In summary, the discrepancy between the HRVO C emission measured in the field and those included in inventories has improved to some degree between the 2000 and 2006 TexAQS studies. The measured emissions have decreased by about 40%, while the inventoried emissions (at least as indicated by the li mited Special 2006 Hourly Emission Inventory) have increased by Figure C8 . HRVOC emissions, by process unit type a nd compound/group. The special 2006 Hourly Emission Inventory is compared to the Ozone Season Daily Emission Inventory (OSD EI) summed over the 32 days (15 August – 15 September) included in the special inventory.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 55 about a factor of 2. However, in spite of this progress, the latest available emission inventories still underestimate ethene emissions by a pproximately an order of magnitude.

Finding C5 : Inventories for NO x point sources at petrochemi cal facilities equipped with CEMS appear to be relatively accurate. Substantial decreases in NO x emissions in the Houston Ship Channel are suggested by the in ventories, and measurements from aircraft are qualitatively consistent with the NO x decreases. Analysis: Trainer et al.-NOAA; Data: Ryerson, et al.-NOAA. Data from the WP-3D flights in 2006 compared to similar data from the Electra aircraft in 2000 indicate that substantial reductions in NO x emissions from certain petrochemical facilities have occurred in the intervening period. Figure C9 pr esents an example of a transect downwind of HSC that was flown under similar conditions du ring both field studies. Plumes with welldefined correlations between NO y and CO 2 concentrations were seen in both years from the east end of the HSC and from the Cedar Bayou complex (at the northern end of Galveston Bay). The slopes of these correl ations give the NO x /CO 2 emission ratio from the respective sources. These observed slopes compared well to the ratios calc ulated from the CEMS systems for the times of emissions of these plumes. It is clear that the NO x /CO 2 emission ratio decreased dramatically for the Cedar Bayou complex (by a factor of 6) as a result of NO x emission controls implemented at this plant. NO x emissions also decreased detectably ( by about 30%) from the facilities at the eastern end of the HSC.

Figure C9 . Relationships between NO y and CO 2 from flights during TexAQS 2000 and 2006. Linear least squares fits are given for correlations with r 2 > 0.80. Slopes of the fits give the NO x to CO 2 emission ratios.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 56 KEY CITATIONS AND INFORM ATION AND DATA SOURCES Murphy, C.F. and D.T. Allen. 2005. Hydrocarbon emi ssions from industrial release events in the Houston-Galveston area and their impact on ozone formation. Atmos. Environ. 39:3785– 3798. Parrish, D.D. 2006. Critical evaluation of US on -road vehicle emission inventories. Atmos. Environ. 40:2288–2300. Ryerson, T.B., M. Trainer, W.M. Angevine, C. A. Brock, R.W. Dissly, F.C. Fehsenfeld, G.J. Frost, P.D. Goldan, J.S. Holloway, G. Hübl er, R.O. Jakoubek, W.C. Kuster, J.A. Neuman, D.K. Nicks, Jr., D.D. Parrish, J.M. Roberts, D.T. Sueper, E.L. Atlas, S.G. Donnelly, F. Flocke, A. Fried, W.T. Potter, S. Schauffler , V. Stroud, A.J. Weinheimer, B.P. Wert, C. Wiedinmyer, R.J. Alvarez, R.M. Banta, L. S. Darby, and C.J. Senff. 2003. Effect of petrochemical industrial emissi ons of reactive alkenes and NO x on tropospheric ozone formation in Houston, Texas. J. Geophys. Res. 108(D8), 4249, doi:10.1029/2002JD003070. Sather, M.E. and K. Cavender. 2007. Trends an alysis of ambient 8 hour ozone and precursor monitoring data in the south centr al U.S. J. Environ. Monit. 9:143–150.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 57 

### Response to Question D

QUESTION D What distribution of anthropogenic and biogeni c emissions of ozone and aerosol precursors can be inferred from observations?

BACKGROUND Questions C, D, and E all deal with emissions. Question C specifically addresses highly reactive VOC and co-located NO x emissions in the Houston area. Here, Question D addresses all other ozone and aerosol precursor emissions, biogenic as well as anthropogenic, that are included in emission inventories. Question E addresses ev idence for additional, unrecognized sources of precursor emissions.

FINDINGS

Finding D1a : Several rural electric generation uni ts (EGU) in the Houston area and in eastern Texas have substantially decreased their NO x emissions per unit power generated since the TexAQS 2000 study. With one exception, SO 2 emissions have not changed appreciably since 2000 for the plants sampled in 2006.

Finding D1b : Comparisons of emissions derived fr om ambient observations with those measured by continuous emission monitoring systems (CEMS) indicate that the emissions from point sources equipped with CE MS are very accurately known.

Finding D1c : Underreporting of CO emissions at se veral EGU noted in 2000 (Nicks et al., 2003) has been reconciled by large increases (by fa ctors of 5 to 50) in the inventory values between 2000 and 2006, as a result of newly impl emented CEMS monitoring of CO at these plants. Analysis and Data: Ryerson et al.-NOAA.
NO x , SO 2 , CO, and CO 2 are emitted directly, in varying ratios, from electric generation units (EGU). Enhancement of the first three species, relative to CO 2 enhancements when sampled in plumes immediately downwind of EGU point sources, provide a measure of pollutant emissions per unit energy generated by the plant (e.g., Ryers on et al., 2003). Comparisons of emissions ratios between the TexAQS 2000 and TexAQS 2006 studies permit an assessment of EGU emissions control strategies, intended primarily to reduce NO x emissions, that have been implemented since 2000. Near-field plumes from numerous rural EGU in Texas were characterized using aircraft measurements in 2000 and 2006. Data were genera ted as shown in Figure D1a, which depicts the NOAA WP-3D ground track for a flight designe d to assess several large EGU point sources in the eastern Texas area. The data are taken fr om the closest transects, within 10 km downwind of the plants, and plotted in Figure D1b as enhancement ratios versus CO 2 . The slopes of linear fits to these data provid e a direct measure of the plant emissions ratios. Analyses of rural EGU emissions ratios to CO 2 have been performed for Monticello, Welsh, Martin Lake, Big Brown, and W.A. Parish power plants. Table D1 compares derived emission ratios from the 2006 WP-3D data with the ratios measured from the NCAR Electra aircraft in 2000.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 58 The TexAQS 2000 study demonstrated quantitative agreement between emissions estimates from the Electra aircraft data and the tabulated emissions from Continuous Emissions Monitoring Systems (CEMS) data, for NO x and SO 2 at each plant. Some variability is expected on time scales of hours to years, and is reflected in the data in Table D 1. Nonetheless, conclusions from the 2006 study can be drawn that are well outside of the uncertainties due to normal emissions variability over time.

34.0

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-95.5 -95.0 -94.5 -94.0 Longitude Monticello
Welsh

Martin Lake 10

8

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4

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0NOx emissions, 1024molec/sec

Figure D1a. Sept. 16, 2006 WP-3D ground track (green) with observed SO2 enhancements (blue) plotted along the track. 120

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0NOx andSO2, ppbv

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60CO, ppbv SO2 CO CO2 NOx

Welsh Monticello

Figure D1b. Time series of CO2, SO2, NOx, and CO measured immediately downwind of Welsh and Monticello.

Table D1 . Measured emissions relative to CO 2 for EGU in East Texas.

NCAR Electra

aircraft data 2000 NOAA WP-3D aircraft data 2006 EGU name SO 2 CO NOx SO 2 CO NOx NOx emissions decreased by factor of: Monticello 3.5 6.4 1.0 2.8 5.4 0.80 1.25 Welsh 1.5 1.7 0.80 1.7 1.7 1.20 1.5 (increase) Martin Lake 1.4 4.0 1.3 3.0 6.1 0.80 1.6 Big Brown 4.8 2.9 1.5 7.8 6.8 0.66 2.3 W.A. Parish 2.1 (variable) 0.88 2.1 (variable) 0.25 3.5 Emissions values presented as mo lecules per 1000 molecules of CO 2 emitted.

Analysis of all available transects of the Parish power plant plume shows that inventory values derived from CEMS data and estimates from th e WP-3D are in quantitative agreement. Figure D2 shows that both the magnitude and the variab ility in time of emissions are captured by the data in Texas 2006 for both SO 2 and NO x .Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 59 3.0

2.5

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9/21/06 10/1/06 10/11/06 Date 

### SO2/CO2 CEMS P-3 data 0.6

0.5

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0.2NOx/CO2(ppb/ppm)

9/21/06 10/1/06 10/11/06 Date 

### NOx/CO2 CEMS P-3 data

Conclusions from this body of evidence are:

• NO x emissions have decreased substantia lly in several EGU, by factors of 2–4, qualitatively consistent with NO x controls implemented since the 2000 study. NO x emissions from other EGU are essentially unchanged.

• SO 2 emissions are generally unchanged betw een 2000 and 2006 in the plants studied to date. The small variability observed is with in that expected from CEMS data due to normally changing plant loads. An excepti on is the Martin Lake plant, where SO 2 appears to have increased by a factor of 2 relative to CO 2 compared to the 2000 study.

• Large CO emissions discrepancies, noted in the 2000 study, have been reconciled by substantial increases in the inventory CO values for several EGU in east Texas.

• Quantitative agreement between inventory values and aircraft estimates of NO x /CO 2 and SO 2 /CO 2 emissions ratios from rural EGU sugge sts that emissions from point sources equipped with CEMS are very well known.

Finding D2 : On-road mobile emission invent ories developed from MOBILE6 have significant shortcomings. MOBILE6 consistently overestimates CO emissions by about a factor of 2. It accurately estimated NO x emissions in the years near 2000, but it indicates decreases in NO x emissions since then, while ambient data suggest NO x emissions have actually increased. Consequently in 2006, NO x to VOC emission ratios in urban areas are likely underestimated by current inventories. Analysis: Parrish et al.-NOAA; Data : TCEQ, Lefer et al.-U. Houston. Figure D3 compares CO to NO x ratios from ambient measurements with those from emission inventories. The Dallas and Houston routine ambi ent data are in excellent agreement with the nationwide AIRS data. The TexAQS 2006 data from Moody tower agree reasonably well with the routine monitoring data. Significant differen ces are seen in El Paso and San Antonio, which have older vehicle fleets. Figure D2 . Ratios of SO 2 and NO x to CO 2 emitted from the Parish power plant plume during six weeks of the TexAQS 2006 study. The ratios de rived from CEMS data are compared to the determinations from multiple near-field transects of the emission plume by the WP-3D aircraft.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 60 The HGB inventory overestimates the CO to NO x emission ratio, and that overestimate becomes worse with time as the inventory does not s how a significant temporal decrease. The HGB points are from the “SIP Quality” emission inventories ( www.tceq.state.tx.us/assets/public/implement ation/air/am/committees/pmt_set/20070424/20070 424-rthomas-0506EI_Modeling_Update.pdf ; accessed 17 August 2007). It should be noted that the on-road emissions invent ory value for 2000 was calculate d with actual data for 2000, whereas the on-road mobile inventories for la ter years are projections, based upon projected population growth, economic data, and estimates of expected emission decreases. The projections, therefore, are more uncertain than the base case 2000 emissions. Parrish (2006) showed that the rapid decrease (6.6 %/yr) in the ratio is partially due to a slower decrease in CO emissions (4.6%/yr), which implies a significant increase in NO x emissions (approximately 2%/yr). The large inventory ove restimates in the CO to NO x ratio at the present time are attributed to a factor of 2 overestimate in CO emissions, and an underestimate in present NO x emissions. This will cause NO x to CO emission ratios in urban areas, which are often dominated by on-road mobile emissions, to be underestim ated by current emission inventories. Urban emission ratios sampled by the WP-3D aircraft in 2006 and the NCAR Electra aircraft in 2000 are consistent with measur ements carried out at a H ouston highway tunnel in 2000 (McGaughey et al., 2004). These measurements demonstrate the weekday increase in CO/CO 2 and CO/NO x emission ratios from midday to the after noon rush hour correlated with increases in the proportion of gasoline vehicles during rush hour. While aircraft and tunnel measurements were not made during the morning rush hour, th eir afternoon rush hour emission ratios are consistent with the routine monitoring data for the morning rush hour. Similar to the routine monitors, the aircraft and tunnel observations in dicate that inventories overestimate mobile source CO by at least a factor of 2.

The VOC to NOx ratio in on-road emissions is of more critical impor tance to photochemical modeling in the HGB area than is the CO to NO x ratio. Parrish (2006) showed that VOC Figure D3 . Determination of CO to NO x ratio in Texas on-road mobile emissions from monitoring data (solid symbols) compared to the HGB emission inventory (open symbols), color-coded according to urban area. The black symbols are for all stations in the EPA AIRS network.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 61 emissions from on-road vehicles have decrease d at a similar rate to CO emissions. This correspondence is expected because catalytic conv erters are the principal control measure for both species. Although MOBILE6 accu rately calculated the VOC to NO x ratio in 2000, the decrease in the ratio is not accurate ly captured. Consequently, in 2006, NO x to VOC emission ratios in urban areas are likely undere stimated by current inventories.

Finding D3 : Emissions from ships co nstitute a significant NO x source in the HGB region. Literature results provide accurate emis sion factors for inventory development. Analysis and Data: Williams et al.-NOAA. During TexAQS 2006, emissions in exhaust plumes from over 200 marine vessels in Galveston Bay and the Houston Ship Channel were measured on the Ronald H. Brown . Table D2 presents the average derived emission factors for slow speed diesel (SSD) engines, which are those with maximum power greater than ~10 MW, and medium speed diesel (MSD) engines, which are of lower power. The NO x values are within 20% of the average values reported in the Lloyd's (1995) study for both MSD and SSD engines although the measured variability is large in both cases. These data are sufficient to provide em ission factors classified by ship type (e.g., freighters, container ships, tankers, tugs, etc.). It is concluded that Lloyd's (1995) provides an accurate characterization of NO x emissions from underway vessels in the HGB region.

Table D2 . Summary of average marine vessel emission factors. Engine type NO x CO SO 2 H 2 CO LAC Medium speed diesel (MSD) 60 8.7 9.1 0.17 0.41

Slow speed diesel (SSD) 74 6.6 28 0.20 1.16 All units are grams of species per kilogram fuel consumed. Emission factors for CO are median data.

The emission factors for CO are within 20% of th e value reported by Lloyd's (1995). There is no trend of increasing CO at lower vessel speeds (use d as a surrogate for engine load), which was seen in the Lloyd's data. Measurements of formaldehyde (H 2 CO) emissions from ships show little distinction between MSD a nd SSD engines; emission of H 2 CO is less than 5% of the emission of CO. Emission factors for SO 2 vary with fuel sulfur cont ent. In the HGB region the mean fuel S derived from the measurements is 0.46% for MSD engines and 1.4% for SSD engines. Measurements of light absorbing carb on (LAC; also known as black carbon) were also derived. Given detailed activity data (i.e., marine fuel consumption) for ships in the HGB region, an emissions inventory for this source could be constructed. The 2007 report from Eastern Research Group (ERG) to TCEQ (Eastern Research Group, 2007) has such data, but there appears to be a significant underestimate (factor of 2-8) of fuel consumption, when compared to an estimate from a more comprehensive model (Wang et al., 2007). The NO 2 emission factors used in the ERG report agree with our data to within 10%. Thus, for current ship emissions inventory modeling, there is less uncertainty co ntributed by emission factors than by activity data.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 62 Table D3 gives emissions of NO x , CO, and SO 2 , relative to the emission of CO 2 , from ships, compared to similar emission ratios from elect ric power generating units (EGU) from the 2004 point source emission inventory, updated to 2006 with CEMS data. This comparison shows that ships emit 10 to 100 times more NO x (and somewhat more CO, and SO 2 ) per unit fuel burned (i.e. CO 2 emitted) than large stationa ry sources. Though the emissions from an individual vessel might be 10-100 times lower than from an EGU, th e volume of ship traffic in the HGB region is sufficient that emissions from co mmercial shipping, in aggregate, cannot be neglected. Accurate fuel consumption or other ship activity data are needed to accurately qua ntify these emissions. Importantly, while emissions from stationary sources are a focus of ongoing control measures, emission controls on commercial shipping are not likely to be implemented because of technical constraints and complications arising from international law.

Table D3 . Comparison of marine ves sel and power plant emissions.

Emission source NO x /CO 2 CO/CO 2 SO 2 /CO 2 Commercial vessel, SSD 22 6.1 6.1 Commercial vessel, MSD 18 8.1 2.0

Electric power generating unit W. A. Parish 0.23 0.51 1.9 Welsh 0.88 1.4 1.8 All units are molecules of species per 1000 molecules of CO 2 .

Finding D4 : Mixing ratios of isoprene over Te xas measured from the WP-3D were used for evaluation of the BEIS-3 emission invent ory. On average, the isoprene emissions from the inventory and emissions derived from the measurements agree within a factor of ~2. There may be areas south of Dallas-Fort Worth and southwest of Houston, where isoprene emissions are lower than indicated by th e BEIS3 inventory based upon the biogenics emission land cover data (BELD-3.0). Analysis and Data: de Gouw, Warneke et al.-NOAA. Isoprene was measured from the WP-3D aircra ft both in-situ by PTR-MS and off-line by laboratory analysis of whole air samples (WAS) co llected in flight. Both measurements agreed within the respective measurement uncertainties. The PTR-MS has the highest time resolution, and thus spatial resolution, and the data illustrate the large variability of the isoprene concentration due to the in-homogeneity of it s sources and its extremely short photochemical lifetime. The PTR-MS data allowed the isoprene emissions to be estimated along the flight track using the measured height of the boundary layer (BL) a nd the concentration of OH radicals calculated from the parameterization of Ehhalt et al. (1998). The emissions were then converted to standard temperature and photo-active radi ation using the formalism from Guenther et al. (1995) and compared to the BEIS3 emission inventory extrac ted along the flight track. The results for a flight on September 16 th over NE Texas are shown in Figure D4. On this flight, the isoprene emissions from BEIS3 and those estimated from the aircraft data agree with in a factor of ~2. A better agreement cannot be expected given the uncer tainties in some of the relevant factors for this comparison (BL height, OH concentratio ns, and vertical gradients in the BL).Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 63 We also used the BEIS3 isoprene emission inve ntory as input for the Lagrangian particle dispersion transport model FLEXPART (Stohl et al., 2003). A comparison between isoprene measured by PTR-MS and modeled using FLEXPART after an average trans port time of 1 hr is shown in Figure D5. As before, the model and meas urements agree within a factor of ~2. Work is in progress to compare measured isoprene mi xing ratios to 3-D chemistry-transport models; however, for a good comparison the models need to get boundary layer heights and OH levels correct, which is not true in all cases. Figure D4 . Isoprene emissions for a flight of the NOAA WP-3D on 16 September over NE Texas. Emissions extracted from the BEIS3 inventory based upon the biogenics emission land cover data (BELD-3.0) along the flight track are shown in red. Emissions derived from the PTR-MS data are shown in blue.

Figure D5 . Comparison between isoprene mixing ratios measured by PTR-MS from the WP-3D on 16 September, and t hose modeled by FLEXPART.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 64 Comparisons between isoprene measurements a nd FLEXPART model results have been made for all flights. Figure D6 shows a map of the flight tracks in the boundary layer color-coded by the differences between measurements and model. The flight tracks in red indicate areas where measurement and model agree well. Flight tr acks in yellow indicate areas where modeled isoprene was higher than the measurements; in partic ular to the southwest of Houston there is an area where this is the case. A possible explana tion includes a difference between the actual land use and the land use assumed in the BEIS3 inventor y; work is in progress to investigate this possibility. There is also an area in between Dallas-Fort Wo rth and Houston where the model indicates higher isoprene than the measurements . This area, however, was not as frequently sampled. Flight tracks in black indicate areas where the modeled isoprene was lower than the measurements. This is the case for NE Texas, but because isoprene was very high over this region, the difference of -400 pptv only repr esents a small relative difference between measurements and model.

Figure D6 . Flight tracks of the NOAA WP-3D aircraft in the boundary layer color-coded by the difference between modeled and me asured isoprene mixing ratios.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 65 Finding D5 : The speciation of VOC from mobile sources in the Houston and Dallas-Forth Worth areas agrees with detailed measurements in the northeastern U.S. However, initial results suggest that the agreement with the NEI-99 emission inventory is poor. Analysis and Data: de Gouw, Warneke et al.-NOAA. For specific wind directions, the emissions from urban, mostly mobile, sources in Houston are spatially separated from industr ial emissions from the Ship Channel area. Specific WP-3D flights were selected based on wind direction to determine the speciation of VOC from mobile sources, using airborne data from the PTR-MS and WAS instruments. In addition, data from the flights downwind from Dallas-Fort Worth were studied. Emissions ratios of VOC versus CO were calculated from the airborne data and compared with the results of a similar study downwind from New York City in 2004 for different classes of compounds (Figure D7). There was a good quantitative agreement between urban emissions in Houston, Dallas, and New York City, which is not surprising. Warneke et al . (2007) showed previously that the VOC composition of urban emissions in New York City and Boston are not represented well in the NEI 99 emissions inventory. Initial results suggest that the same is true for Houston and DallasFort Worth.

Figure D7. Emissions ratios (ER) of various VOC to CO from urban, mostly mobile, sources in Houston and Dallas-Fort Worth in 2006 versus the same ratios from New York City in 2004.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 66 KEY CITATIONS AND INFORM ATION AND DATA SOURCES Eastern Research Group. 2007. Houston/Galvesto n Routine Vessel Identification and Traffic Study, ERG Rep. No. 0196.00.023, TCEQ Contract No. 582-04-65564-23, January 26, 2007. Ehhalt, D.H., F. Rohrer, A. Wahner, M.J. Prather, and D.R. Blake. 1998. On the use of hydrocarbons for the determination of tropos pheric OH concentrations, J. Geophys. Res. 103:18981-18997. Guenther, A., C. Hewitt, D. Erickson, R. Fall, C. Geron, T. Graedel, P. Harley, L. Klinger, M. Lerdau, W. McKay, T. Pierce, B. Scholes, R. St einbrecher, R. Tallamraj u, J. Taylor, and P. Zimmerman. 1995. A global model of natural vo latile organic compound emissions. J. Geophys. Res. 100(D5):8873-8892. Lloyd's Register Engineering Services. 1995. Ma rine Exhaust Emission Research Programme. Lloyd's Register House, London. McGaughey, G.R., N.R. Desai, D.T. Allen, R.L. Seila, W.A. Lonneman, M.P. Fraser, R.A. Harley, J.M. Ivy, and J.H. Price. 2004. Analys is of motor vehicle emissions in a Houston tunnel during the Texas Air Qualit y Study 2000. Atmos. Environ. 38(20):3363-3372. Nicks Jr., D.K., J.S. Holloway, T.B. Ryerson, R.W. Dissly, D.D. Parrish, G.J. Frost, M. Trainer, S.G. Donnelly, S. Schauffler, E.L. Atlas, G. Hübler, D.T. Sueper, and F.C. Fehsenfeld. 2003. Fossil-fueled power plants as a source of at mospheric carbon monoxide . J. Environ. Monit. 5:35-39. Parrish, D.D. 2006. Critical evaluation of US on -road vehicle emission inventories. Atmos. Environ. 40(13):2288-2300. Ryerson, T.B., M. Trainer, W.M. Angevine, C. A. Brock, R.W. Dissly, F.C. Fehsenfeld, G.J. Frost, P.D. Goldan, J.S. Holloway, G. Hübl er, R.O. Jakoubek, W.C. Kuster, J.A. Neuman, D.K. Nicks, Jr., D.D. Parrish, J.M. Roberts, D.T. Sueper, E.L. Atlas, S.G. Donnelly, F. Flocke, A. Fried, W.T. Potter, S. Schauffler , V. Stroud, A.J. Weinheimer, B.P. Wert, C. Wiedinmyer, R.J. Alvarez, R.M. Banta, L. S. Darby, and C.J. Senff. 2003. Effect of petrochemical industrial emissi ons of reactive alkenes and NO x on tropospheric ozone formation in Houston, Texas. J. Geophys. Res. 108(D8), 4249, doi:10.1029/2002JD003070. Stohl, A., C. Forster, S. Eckhardt, N. Spichtinge r, H. Huntrieser, J. Heland, H. Schlager, S. Wilhelm, F. Arnold, and O. Cooper. 2003. A b ackward modeling study of intercontinental pollution transport using aircraft measurements. J. Geophys. Res. 108, 4370, doi:10.1029/2002JD002862. Wang, C., J.J. Corbett, and J. Firestone. 2007. Modeling energy use and emissions from North American shipping: Applicati on of the ship traffic, energy, and environment model. Environ. Sci. Technol., doi:10.1021/es060752e. Warneke, C., S.A. McKeen, J.A. de Gouw, P. D. Goldan, W.C. Kuster, J.S. Holloway, E.J. Williams, B.M. Lerner, D.D. Parrish, M. Trainer, F.C. Fehsenfeld, S. Kato, E.L. Atlas, A. Baker, and D.R. Blake. 2007. Determination of urban volatile organic compound emission ratios and comparison with an emissions database. J. Geophys. Res. 112, D10S47, doi:10.1029/2006JD007930.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 67 

### Response to Question E

QUESTION E Are there sources of ozone and aerosol precurs ors that are not represented in the reported emissions inventories?

BACKGROUND Questions C, D, and E all deal with the ev aluation of emission inve ntories. Question C specifically addresses highly re active VOC and co-located NO x emissions in the Houston area. Question D addresses all other ozone and aeros ol precursor emissions , biogenic as well as anthropogenic, that are included in emission inventories. Here , Question E addresses evidence for ozone and aerosol precursor emissions not presently represented in inventories.

FINDINGS

Finding E1: The observed mixing ratios and regional distribu tion of ambient formaldehyde are broadly consistent with da ytime photochemical production from reactive VOC. An upper limit for prim ary formaldehyde emissions from mobile sources is obtained from nighttime measurements, and is small in comparison with the secondary, daytime formation. Analysis: de Gouw et al.-NOAA, Herndon et al .-Aerodyne, Rappenglück et al.-U. Houston; Data: Herndon et al.-Aerodyne, Fried et al.-NCAR, de Gouw et al.-NOAA, Atlas et al.-U. Miami, Rappenglück et al.-U. Houston.
Diurnal variations in the concentrations of et hene, propene, and formaldehyde were measured at several locations in the HSC. Figure E1 shows the data from Barbour’s Cut, at the eastern end of the HSC. The mixing ratios of ethene and propene (VOC from direct emi ssion) were highest at night, when photochemistry was absent and emissi ons of these species accumulated in a shallow boundary, and lowest during the day, when the boundary layer was deeper and alkenes were removed by reaction with OH. In contrast, form aldehyde is highest during the day – indicating that daytime photochemistry was th e dominant source of this ozone precursor in the HSC area. Similar diurnal patterns also were observed at the Moody Tower site at the University of Houston.

Figure E1. Average diurnal variation of mixing ratios of ethene, propene, and formaldehyde measured from the Ronald H. Brown over approximately ten days of measurements in Barbour’s Cut.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 68 Nighttime measurements onboard the Ronald H. Brown ( RHB ) and the WP-3D and at the Moody Tower site provide a useful means to estimate th e direct emissions of fo rmaldehyde from mobile sources. The left panel of Figur e E2 shows the relationship of formaldehyde to CO measured in the turning basin at the western end of the HSC with winds coming from the central urban Houston area. The data are co lor-coded by the measured photolys is rate of formaldehyde; the nighttime points, in black, show a clear correla tion between formaldehyde and CO with a slope of about 3 pptv ppbv -1 . After sunrise, the formaldehyde to CO ratio increased strongly due to photochemical formation of formaldehyde (daytime points in yellow). Similar observations were made onboard the WP-3D during a missed approach at Montgomery airport to the north of Houston during the night (Figure E2, right panel). In this gr aph the data are color-coded by altitude. Below 200 m the aircraft penetrated a shallow layer that was strongly impacted by local, primary emissions; the formaldehyde to CO ratio in this layer was 1.8 pptv ppbv -1 . Above the layer, the aircraft sampled the remnants of th e daytime boundary layer that is isolated from primary emissions, and the formaldehyde to CO ratio was much higher. The corresponding slope from the Moody Tower measurements is somewhat higher (5 to 7 pptv ppbv -1 ); the cause and implications of this larger slope is under investigation. From these observations we estimate the primary emissions of formaldehyde from mob ile sources to be 0.18-0. 3 percent of the CO emissions; this estimate is an upper limit, since it is not possible to exclude the possibility that the sampled air had been photochemically processe d to at least some extent during the preceding daytime period. These findings are consistent wi th the conclusions of Anderson et al. (1996) who found formaldehyde to CO em ission ratios of 0.10 to 0.14 percen t in measurements made in Denver, Colorado.

Figure E2. Relationships of HCHO to CO c oncentrations obtained onboard the Ronald H. Brown in the turning basin at the western end of the HSC (left panel), and from the WP-3D during a nighttime, missed approach at Montgomery County airport to the north of Houston.

Finding E2: Concentrated plumes of ammonia were ob served occasionally in the Houston Ship Channel area. These plumes often led to the formation of ammonium nitrate aerosol. Analysis and Data: Nowak et al.-NOAA.
Ammonium nitrate aerosol is formed from the reaction of gas-phase ammonia (NH 3 ) and nitric acid (HNO 3 ). Anthropogenic emissions of NH 3 and NO x , which in sunlight is oxidized to formFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 69 HNO 3 , can result in elevated concentrations of ammonium nitrate. Sources of NH 3 in the Houston area are thought to incl ude motor vehicles, industrial f acilities, outlying agricultural activity, and possibly power pl ants. High-time-resolution ( ≈ 1 s average) NH 3 measurements were made from the WP-3D aircraft by a Chemic al Ionization Mass Spectrometry technique and from the Ronald H. Brown by quantum cascade laser absorpti on during TexAQS 2006 with the goals of characterizing sources and examining the effect of NH 3 on atmospheric aerosol formation. Figure E3 shows the altitude prof ile of all 1-second average NH 3 mixing ratios measured aboard the WP-3D aircraft during the TexAQS 2006 study. Typically, NH 3 mixing ratios over the urban area ranged from 0.2 to 3 ppbv, and generally de creased with increasi ng altitude. Though infrequent, plumes with NH 3 mixing ratios from 5 to greate r than 50 ppbv (highest values not indicated in Figure E3) were obser ved in the boundary layer below 1 km altitude. These plumes were encountered over the H ouston metropolitan area in the vicinity of the HSC, around Beaumont, and in St. James Parish, Louisiana. NH 3 mixing ratios as high as several hundred ppbv were measured in the HSC from the RHB

.

It is difficult to trace the s ources of plumes with high NH 3 concentrations to particular industrial facilities. Figure E4 illustrates observations made in one of these plumes intercepted approximately 13 km downwind from the Cedar Ba you Station power plant. This plant is the location of the only significant upwind NH 3 point source listed in the National Emission Inventory of 1999 (NEI99v3). However, the lack of a strong correlation of the concentration of NH 3 with the enhancements of the CO 2 and NO x concentrations (upper panel of Figure E4) suggests the NH 3 is not released in the Cedar Bayou stack emissions. Future analysis will focus Figure E3 . TexAQS 2006 NH 3 observations from the WP-3D plotted as function of altitude.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 70 on using the 2006 Toxic Release In ventory along with measuremen ts of VOC made aboard the WP-3D aircraft to unambiguously identify this NH 3 plume source.

The observed NH 3 enhancements were typically found to be accompanied by corresponding increases in the particulate nitrate (NO 3 - ) and decreases in HNO 3 mixing ratios as illustrated in the bottom panel of Figure E4. These correlated variations indi cate the formation of ammonium nitrate. The magnitude of the observed HNO 3 lost and NO 3 - formed is consistent (within a factor of 2) with ammonium nitrate formation. The ai r quality implications of such ammonia plumes should be considered, particularly during cooler wintertime months when the ammonium nitrate will make a longer-lived contribution to the PM 2.5 concentrations. Power plants that have installed Selective Cata lytic Reduction (SCR) units constitute a possible NH 3 source. This process adds aqueous NH 3 to the exhaust gases as a reagent to decrease NO x emissions. NH 3 “slippage,” i.e., unwanted emissions of NH 3 into the atmosphere, occurs when exhaust gas temperatures are too low for the SCR reaction to proceed to completion, or when too much NH 3 is added. The W.A. Parish electric generating facility is equipped with these units. To characterize emissions from this plant, th e WP-3D aircraft sampled the Parish plume on numerous flights during TexAQS 2006; Figure E5 shows a time series of measurements from one example transect of the plum e. Clear enhancements of CO 2 , NOy, and SO 2 coincide with a depletion in the ozone concentration due to its reaction with the freshly emitted NO x , while no difference in NH 3 (black line) mixing ratios can be discerne d in or out of the plume. The lack of NH 3 enhancement in the power plant plumes sampled by the WP-3D indicates that NH 3 slippage was not significant during any of th e TexAQS 2006 plume transects. Figure E4. Time series of concentration measurements of primary emissions (NH 3 , NO x , CO 2 ) and secondary products (HNO 3 nitrate aerosol) during a transect of the Cedar Bayou power plant plume.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 71 Finding E3: Concentrated plumes of gaseous elemen tal mercury from at least one point source were observed repeatedly in the Ho uston Ship Channel area and once in the Beaumont-Port Arthur area. The sources of the plumes could not be identified with current inventory sources of mercury. Analysis and Data: Ryerson et al.-NOAA. High-time-resolution ( ≈ 1 s) measurements of gaseous elemental mercury (Hg) were made from the Ronald H. Brown during TexAQS 2006. Figure E6 shows examples of the plume encounters in the HSC and Beaumont industrialized area. The magnitude of the detected plumes varied widely; the HSC plume in Figure E6 was detected during each of four transects of the HSC under southerly to easterly winds, but the magn itude of the plume varied by a factor of approximately 25. During the plume transects, th e measured Hg concentration did not correlate with the measured concentrations of any other m easured species. This lack of correlation rules out most known sources of Hg emissions, which can be expected to co-emit one or more of the wide range of chemical or aerosol species that were m easured concurrently on the RHB . Further, the measured Hg concentrations are not consiste nt with the latest TCEQ or EPA AIRS, eGRID, and TRI inventory source locations for known Hg sources in the HSC regi on. A potential source not included in these inventories would be re-releas e of Hg from historically contaminated soils. More research into this possi bility is needed to better understand the unexpectedly high concentrations of Hg observed in coasta l industrial areas in eastern Texas. Figure E5 . Time series of NH 3 , NOy, SO 2 , CO 2 , and O 3 observations during transect of the W.A. Parish power plant plume.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 72 80

60

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20

0

8:30 PM 8/9/06 8:40 PM Time, UTCBeaumontPort Arthur 1-second Hg(0) data 250

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0Hg, ng/m3

7:00 PM 8/2/06 7:04 PM 7:08 PM Time, UTC Houston Ship Channel 1-second Hg(0) data

KEY CITATIONS AND INFORM ATION AND DATA SOURCES Anderson, L.G., J.A. Lanning, R. Barrell, J. Mi yagishima, R.H. Jones, and P. Wolfe. 1996. Sources and sinks of formaldehyde and acetal dehyde: An analysis of Denver’s ambient concentration data. Atmos. Environ. 30:2113-2123. Ryerson, T.B., M. Trainer, W.M. Angevine, C. A. Brock, R.W. Dissly, F.C. Fehsenfeld, G.J. Frost, P.D. Goldan, J.S. Holloway, G. Hübl er, R.O. Jakoubek, W.C. Kuster, J.A. Neuman, D.K. Nicks, Jr., D.D. Parrish, J.M. Roberts, D.T. Sueper, E.L. Atlas, S.G. Donnelly, F. Flocke, A. Fried, W.T. Potter, S. Schauffler , V. Stroud, A.J. Weinheimer, B.P. Wert, C. Wiedinmyer, R.J. Alvarez, R.M. Banta, L. S. Darby, and C.J. Senff. 2003. Effect of petrochemical industrial emissi ons of reactive alkenes and NO x on tropospheric ozone formation in Houston, Texas. J. Geophys. Res. 108(D8), 4249, doi:10.1029/2002JD003070. Wert, B.P., M. Trainer, A. Fried, T.B. Ryerson, B. Henry, W. Potter, W.M. Angevine, E. Atlas, S.G. Donnelly, F.C. Fehsenfeld, G.J. Frost, P.D. Goldan, A. Hansel, J.S. Holloway, G. Hübler, W.C. Kuster, D.K. Nicks Jr., J.A. Neuma n, D.D. Parrish, S. Schauffler, J. Stutz, D.T. Sueper, C. Wiedinmyer, and A. Wisthaler. 2003. Signatures of terminal alkene oxidation in airborne formaldehyde measurements duri ng TexAQS 2000. J. Geophys. Res. 108(D3), 4104, doi:10.1029/2002JD002502.

Figure E6. Measurements of gas-phase elemental merc ury (Hg). Note the change in scales between the two graphs. Periods of missing data are due to automatic instrument calibration or zero procedures. Figure E6. Measurements of gas-phase elemental me rcury (Hg). Note the change in scales between the two graphs. Periods of missing data are due to automatic instrument calibration or zero procedures.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 73 

### Response to Question F

QUESTION F How do the mesoscale chem ical environments (NO x -sensitive ozone formation vs radicalsensitive ozone formation) vary spatially and temporally in Houston, Dallas, and eastern Texas? Which mesoscale chemical environments are mo st closely associated with high ozone and aerosol?

BACKGROUND

Determination of NO X versus VOC (or Radical-Formation) Sensitive Ozone Formation The accurate prediction of the relative response of ozone concentrations to future reductions in NO x and VOC emissions is a much sought, but very el usive goal. The predic tion is central to the very important SIP-relevant question of “directi on of control” – that is, should ozone control efforts in an ozone non-attainment area be focused on: a) decreasing emissions of NO x alone, b) decreasing emissions of VOC alone, or c) decreasing emissions of both NO x and VOC? The following paragraphs discuss some important co nsiderations concerning this question. In the period between the two TexAQS field studies, a change in the NAAQS for ozone was implemented, with at least two important implica tions for evaluating the “direction of control” question. The older standard, based upon a re latively high (120 ppbv) ozone concentration averaged over a short period (one hour), has been supplanted by one based on a lower (80 ppbv) concentration averaged over a longer period (eig ht hours). One implication is that the older, shorter-period average was much mo re amenable to analysis base d upon in situ observations; the “snap shot” of the relationships between simulta neously measured concentrations of ozone, its precursors, and other photochemical products provid ed direct clues to th e limiting precursor in an observed ozone exceedance. The longer-per iod average requires a more comprehensive analysis of the integrated accumulation of oz one over the full 8-hour exceedance period. A second implication is that the background ozone tr ansported into an urban area will constitute a much larger fraction of the ambient ozone conc entration constituting an 8-hour exceedance than was the case for a one-hour exceedance. Thus, much mo re attention must be paid to the effect of control strategies in upwind regions. Some approaches to determining the “directi on of control” questi on rely, in effect, upon comparing the response of ambient ozone to differential reductions in VOC versus NO x precursor emissions. Such approaches give useful guidance for effectively achieving incremental improvements in ozone air quality; ho wever such approaches cannot determine if compliance with the NAAQS can be reached thro ugh such incremental emission reductions, and do not identify the most effective emission control approach for reaching compliance. A rigorous approach to answering all aspects of the “direction of control” question would require a thoroughly tested Eulerian air quality model that treats the re gion of interest with sufficient accuracy. Multiple simulations would then be run with that “perfected” model to test possible NO x and VOC emission control strategies. These simulations would then provide the required ozone response information. Unfortunately however , it is not yet generally possible to develop such models because of deficiencies and uncer tainties in many critical areas, including emissions, meteorological modeling, and photoche mical mechanisms. The thorough testing ofFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 74 the model must verify that the model adequate ly reproduces the observe d ozone for the correct reasons. This testing would in clude at least two model-measur ement comparisons: first, the model’s ability to reproduce obser ved relationships between the c oncentrations of ozone and its precursors and, importantly, other secondary photo chemical products (organic nitrates, nitric acid, formaldehyde and other oxygenated VOC, per oxides), and second, the model’s ability to reproduce the observed ambient c oncentrations of the radicals that drive the photochemical reactions. The first comparison should pay par ticular attention not onl y to the slopes and correlation coefficients of the relationships, but also to the range of the observed and modeled concentrations of ozone and the related species . From this perspect ive, the air quality community is still in the early stages of the testing process; a process that is limited both by resources for model development, and by the ava ilability of observational data with which to compare the models. The eastern Texas region provides particular advantages, as well as particular challenges, for achieving the required model de velopment and application. The TexAQS 2000 and TexAQS II field studies have collected data sets that provide unprecedented opportunities for model testing. However, experience has shown that development of needed emission inventories is a challenge that has not been met in all respects (see discu ssion in Responses to Questions C, D, and E.) A further challenge is presented by the concentrat ed plumes that determine the highest ozone values in the Houston area (see discussion in Re sponse to Question A.) Such resolution may be a severe challenge for Eulerian models, especially if they attempt to include the whole eastern Texas region. At this point it is not possible to provide a rigor ous answer to the “direction of control” question. In the absence of a perfected model, it has b een necessary to take a variety of heuristic approaches to provide guidance to air quality managers. Heuristic implies a model that is simplified, but that is designed and used to lear n more about some specifi c but important aspect of the more complex air-quality system that is to be managed. In this re port the clos ely related Questions F and K both deal with aspects of this issue. Here in Question F the application of both observation and modeling based approaches fo r approximately determining the sensitivity of ozone production will be discussed; the photo chemical environment of the entire eastern Texas region will be considered. Question K presents an EKMA model-based approach to address the same issue, but from a different persp ective; its application is restricted to the HGB

area.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 75 Figure F1 . Relationship between ozone and formaldehyde concentrations observed from the Electra aircraft on 1 September 2000 and modeled by a Lagrangian plume model ( Wert et al.

, 2003 )

. FINDINGS

Finding F1 : Both Eulerian and Lagrangian plum e modeling approaches indicate that in 2000 high ozone concentrations in the HGB area were sensitive to both VOC and NO x emission reductions. Lagrangian Plume Model Analysis: M. Trainer-NOAA; Data: Parrish et al .-NOAA, Fried et al.-NCAR, Atlas et al.-U. Miami. Wert et al. (2003) presented a Lagrangian pl ume model designed to closely reproduce the emissions, ozone formation, other secondary photochemical product formation, and plume dispersion observed during the TexAQS 2000 study. Figure F1 shows that the model accurately reproduced the rapid production of ozone and form aldehyde that was measured in a concentrated plume originating from the HSC region. (Figure A3 shows the location of th is plume intercept). The model required only two HRVOC – ethene and propene. To successfully reproduce the highest observed ozone concentrations, it was criti cal that the model repr oduced two factors: a high ozone production efficiency as shown by observations (e.g. Fi gures A3 and A4 of this report), and a rapid rate of oz one production. High ozone production efficiency assures that high ozone can be produced from the emitted precursors, and the rapid rate of ozone production assures that high ozone is produced before the plumes of emissions dilute and disperse.

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Model

Figure F2 shows the modeled sensitivity of the ozone accumulation to both NO x and VOC emission reductions for the plume illustrated in Figure F1. The upper panel indicates that decreases in NO x emissions lead to a more rapid oz one production, but a lower peak ozone concentration, while HRVOC emission reductions lead to both slower ozone production and a lower peak ozone concentration. The lo wer panel indicates that HRVOC and NO x emission reductions are almost equally effective in reduci ng the total ozone flux in this plume. It is noteworthy that the ozone respons e is highly non-linear; reduction of either precursor by onehalf reduces the ozone flux by only about one-quarter.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 76 The ozone flux produced and transported in this plume from the HSC accounts for a substantial fraction of the total ozone produced in the entire Houston area. In Response to Question G of this report, the total flux from the Houston urban area is calculated from airborne ozone lidar measurements. The base case calculated plume flux in Figure F2 represents 45 to 80% of the total Houston flux determined on six days during TexAQS 2000 and 2006. These calculations led Wert et al. (2003) to conclude that targeted reductions in either or both emission categories would effectively reduce the highest observed ozone levels. Eulerian Modeling TCEQ (2004, 2006) has conducted Eulerian modeling of the entire HGB nonattainment area in order to determine which controls are necessary to reach attainment. This modeling indicates that both VOC and NO x controls were effective in reducing ozone in 2000. In particular, two tests have indicated sensitivity to both VOC and NO x in Houston: reductions of biogenic VOC emissions by 30%, and modeling of weekend/weekday differences in mobile source emissions. However, in both cases, the effects on ozone concentrations varied by location within the HGB area and meteorological conditions, suggesting that VOC and NO x sensitivity in HGB varies spatially and temporally. TCEQ modeling for the HGB area indicates that in 2009 further NO x emission reductions will be by far more effective than VOC em ission reductions in decreasing O 3 . In the 2009 future baseline scenario, the em issions of anthropogenic NO x and VOC are projected to be reduced by 56% and 24%, respectively, relative to th e 2000 emission inventory. Despite these large projected decreases in emissions, the maximum ozone design value in HGB is predicted to be 97 ppbv, well above the 84 ppbv level of the NAAQS . Thus without additional emission reductions, the HGB area is predicted to rema in out of compliance with the NAAQS in 2009. Figure F3 shows the predicted effects of additional anthropoge nic emission reductions beyond the 2009 future baseline. Additional reductions in VOC emissions result in only modest improvement in the ozone design value, and compliance with the NAAQS cannot be achieved with VOC decreases alone. The modeling does s uggest that reductions in VOC emissions would allow compliance with the NAAQS to be reached with somewhat less drastic NO x emission reductions (53% versus 58%) than is the case for NO x emission reductions alone. Figure F2 . Ozone sensitivity calculated by the Lagrangian plume model. The red lines give the base case calculation, and the green and red lines indicate the effect of a factor of two decrease in emissions NO x and VOC, respectively.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 77 “Radical Starvation” in Sel ected Modeling Scenarios A photochemical grid modeling scenario has sh own that under certain conditions with high concentrations of HRVOC, ozone formation can be inhibited by "radical st arvation." The radical starvation can be alleviated by adding large qu antities of primary formaldehyde emissions, CO emissions, or aromatic emissions, or by changing to the SAPRC99 chemical mechanism. It is unclear at this point which, if any, of these solutions are ap propriate for Houston modeling. Figure F3 . Predicted future ozone design values (O3 DVf ; the 3-year average of the fourth-highest daily maximum 8-hour-average ozone concentration) for HGB as a function of reductions in emissions of NO x , VOC, and both. The reference calculation (i.e. 0 percent reduction in the table in the figure) is based upon the projected emissions for 2009, and the reductions from that reference case are assumed constant across all emission categories. (Analysis by TCEQ; Figure adapted from D. Karp)Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 78 Finding F2 : An observation-based approach to determine the sensitivities of high ozone in the HGB non-attainment area to the precursor VOC and NO x emissions has been investigated; it has yielded ambiguous results. Analysis: Parrish-NOAA; Data: Ryer son, Neuman, Parrish et al.-NOAA. The observation-based approach selected is based upon the relationships between indicator species developed by S. Sillman ( http://www-personal.engin.umich.edu/~sillman/obm.htm ). This approach was selected because: 1) it addresses integrated total O 3 produced (not instantaneous rate of O 3 production), and is t hus designed to answer the question of how maximum O 3 responds to changes in VOC versus changes in NO x emissions; 2) it is arguably the most fully developed observation-based method and is widely used; 3) it is related to the ozone production efficiency relationships exemplified in Figures A3 and A4 of this report; and 4) it utilizes measurements made with high precision and accuracy on the Electra aircraft during TexAQS 2000 and the WP-3D and Ronald Brown during TexAQS 2006. Figure F4 compares the measurements made from the Electra during TexAQS 2000 with the modeled indicator species relationships of Sillman. These two relationships are those he discusses most thoroughly. The data shown include all 5- second average measurements made in the greater Houston area (94.3-96.3 deg. E Long., 28.7-30.7 deg. N. Lat.), which includes the entire urban area, all petrochemical facilities including the isolated Gulf Coast facilities, and the Parish power plant.

Figure F4 . Indicator species relationships from models [Sillman] and from the TexAQS 2000 Electra measurements (red symbols). The relationships expected for NO x -, mixed-, and VOC-sensitive ozone production are given by green, violet and black symbols, respectively. The blue highlighted symbols indicate air masses dominated by NO x titration.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 79 The indicator species relationships in Figure F4 gi ve ambiguous results. In both figures most of the data are localized in the predominately NO x -sensitive region, but so me points are in the mixed and predominately VOC-sensitive regions. Further, the model results themselves are interspersed between regions. Definitive conclusi ons cannot be drawn from these figures. This ambiguity is particularly clea r in the examination of ozone data above 200 ppbv. These data were collected on three days in 2000: 25 and 30 August and 1 Septem ber (see Figure A3). The relationship of ozone with HNO 3 suggests that all three days represent NO x -sensitive conditions, while the relationship of ozone with NO y - NO x suggests that the three days span the full range from NO x -sensitive to VOC-sensitive. It is clear that no definitive conclusions can be drawn. It may be that the indicator species relationships ma y be most useful as a basis of comparison of models with measurements.

Finding F3 : At the highest ozone concentrations, the observed relationship between ozone and the products of NO x oxidation indicates less efficien t ozone production in the Dallas area than in the Houston area. In the obs ervation-based indicator species approach, this behavior corresponds to less NO x -sensitive and more VOC- or radical-sensitive ozone formation in Dallas compared to Houston. Analysis: Parrish-NOAA; Data: Ryer son, Neuman, Parrish et al.-NOAA. Figure F5 shows the relationships between ozone and the oxidation products of NO x . These oxidation products include only nitric acid on the left plot and nitric acid plus organic nitrates on the right plot. The generally shallower slopes in the Dallas area indicate less efficient ozone production in that area. From an observati on-based indicator appr oach, this behavior corresponds to less NO x -sensitive and more VOC- or radical -sensitive ozone formation in Dallas compared to Houston.

Figure F5 . Indicator species relationships from the TexAQS 2000 Electra measurements. The gray and blue symbols indicate data collected in the Houston and Dallas areas, respectively.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 80 Finding F4 : Tests of the ability of models to reproduce observed relationships between ozone and other photochemical products have the potential to provide very fruitful approaches to improving models. Analysis: Parrish-NOAA; Modeli ng: TCEQ; Kim et al.-NOAA. Relationships between ozone and other photochemi cal products, such as those illustrated in Figure F5, have been found to be remarkably robu st in observations. Examination of the ability of models to reproduce these observed relationships is expected to provide powerful tests of the models’ performance and to suggest possible mode l improvement. Figures F6 and F7 give some example model output for the relationships of O 3 with HNO 3 and with NO y -NO x . The figures are plotted in the same format as the observa tions in Figure F5 for ease of comparison. The results of the WRF-Chem model (Figure F6), which covers most of the continental U.S. at relatively coarse (27 km x 27 km) resolution (K im et al, 2006), shows some similarities and differences when compared to th e observations (Figure F5). The model produces relatively well defined relationships, but the slopes of the rela tionships are significantly smaller than those observed. There is no marked difference in the m odeled relationships in the different regions of eastern Texas. The very high O 3 concentrations observed in th e Houston area are not reproduced by the model. Significantly higher resolution, part icularly over the urban areas, is an avenue of model development that would be expected to improve the model performance. Further, the HRVOC emissions for the petrochemical facili ties are undoubtedly underestimated in these calculations, since they are based upon the NEI 1999 emission inventory. The TCEQ modeling results (Figure F7) are focused on the HGB region with much higher resolution, and generally give a better reproduction of the observe d relationships. The slopes of the relationships through the majo rity of the data are accurately reproduced. However, there is an indication that the slopes decrease at the highest O 3 concentrations, so that the highest modeled O 3 concentrations are significantly lower than those observed. One explanation that has been suggested for this behavior is that the HRVOC emissions are underestimated in the model emissions and/or that they are not properly collocat ed with the NO x emissions from the petrochemical facilities. The model also pr edicts relatively low concentrations of O 3 at relatively high concentrations of HNO 3 and NO y -NO x . Such behavior is not seen in the observations. The cause of the disagreement in these fe w points should be investigated.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 81 Figure F6 . Indicator species relationships calculated by the WRF-Chem model for 2000 UTC each day between 1 April and 31 October 2004. The color c oding indicates results for different areas of eastern Texas.

Figure F7 . Indicator species relationships derived from 2006 modeling by TCEQ with the Base 1b configuration. The data points include all hours, 12:00-18:00 local time, on the days between 16 August and 6 September 2000. The color coding indicates tw o different monitoring sites in the HGB area.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 82 KEY CITATIONS AND INFORM ATION AND DATA SOURCES Kim, S.-W., A. Heckel, S.A. McKeen, G.J. Frost, E.-Y. Hsie, M.K. Trainer, A. Richter, J.P. Burrows, S.E. Peckham, and G.A. Grell. 2006. Satellite-observed U.S. power plant NO x emission reductions and their impact on air quality. Geophys. Res. Lett. 33, L22812, doi:10.1029/2006GL027749. Sillman, S. Observation-based methods (OBMs) for analyzing urban/regional ozone production and Ozone- NO x -VOC sensitivity ( http://www-personal.engin.umich.edu/~sillman/obm.htm ) TCEQ. 2004. Houston-Galveston- Brazoria Mid-Course Review SIP Appendixes (2004-042-SIPNR). http://www.tceq.state.tx.us/implem entation/air/sip/dec2004hgb_mcr.html TCEQ. 2006. Weekday-Weekend Eff ect Analysis, Part Two. Presented by Jim Smith, June 8, 2006. http://www.tceq.state.tx.us/a ssets/public/implementation/ai r/am/committees/pmt_set/200606 21/20060621-smith-weekday_weekend_effects_part2.pdf Wert, B.P., M. Trainer, A. Fried, T.B. Ryerson, B. Henry, W. Potter, W.M. Angevine, E. Atlas, S.G. Donnelly, F.C. Fehsenfeld, G.J. Frost, P.D. Goldan, A. Hansel, J.S. Holloway, G. Hübler, W.C. Kuster, D.K. Nicks Jr., J.A. Neuma n, D.D. Parrish, S. Schauffler, J. Stutz, D.T. Sueper, C. Wiedinmyer, and A. Wisthaler. 2003. Signatures of terminal alkene oxidation in airborne formaldehyde measurements duri ng TexAQS 2000. J. Geophys. Res., 108(D3), 4104, doi:10.1029/2002JD002502.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 83 

### Response to Question G

QUESTION G How do emissions from local and distant sour ces interact to determine the air quality in Texas? What meteorological and chemical conditions exist when elevated background ozone and aerosol from distant regions affect Texas? How high are background concentrations of ozone and aerosol, and how do they vary spatially and temporally?

BACKGROUND Question G is closely related to Question H. Here Question G focuses on characterizing the background ozone and aerosol distributions, and the chemical and physical processes that affect the background concentrations of ozone and aer osol in Texas. Question H focuses on the transport processes and source-receptor relati onships of those background concentrations.

FINDINGS

Finding G1: The maximum background ozone co ncentrations encountered in 2006 exceeded the 8-hour NAAQS. On average, air of continental origin had higher background concentrations than marine air. The average background ozone concentrations measured in 2006 in eastern Texas complement a previously developed climatology. Analysis: Senff et al.-NOAA, Sullivan et al.- U. Texas; Data: Senff et al.-NOAA, TCEQ. Daily 8-hour-average ozone maxima from upwind s uburban or rural sites ar e taken as indicators of the local background in a particular region. In Figure G1, the red line segments indicate the average background for four areas in Texas on days in April – July 2006 when the area maximum 8-hour average reached or exceeded 80 ppbv. These segments are higher than the corresponding average curves, which were compiled for all days, not just the higher ozone days. The marine influence in the Houston area accounts for the lower background (49 ppbv) compared to the Dallas (63 ppbv), Beaumont (61 ppbv), and Northeast Texas (60 ppbv) areas. The NOAA lidar provided a differe nt approach for obtaining meso scale estimates of background ozone for four regions in eastern Texas: 1) offs hore over the Gulf of Mexi co, 2) Gulf of Mexico coastal areas (less than 50 km from shore), 3) southeast Texas incl uding Houston (all of southeast Texas more than 50 km from shor e), and 4) northeast Texas including Dallas. Background ozone was determined by averaging lidar ozone profiles between the surface and the top of the boundary layer over all data points th at were identified to be outside of pollution plumes. The blue line segments in Figure G1 indicate the background ozon e values for the four regions identified above using measurements from all suitable lidar flights. The average background values for the southeast Texas and Gu lf coast areas compare well with the Houston area background on high-ozone days (red line se gment). The northeast Texas lidar background ozone value is close to the Da llas and northeast Texas TCEQ ne twork-based background values for high-ozone days. This good agreement is not surprising because the lidar was typically flown on days when an ozone exceedance was forecast. The average ozone background measured over the Gulf of Mexico was the lowest of all four areas (39 ppbv) and is close to the average curves for Houston-Galveston-Brazoria (HGA) and Beaum ont-Port Arthur (BPA) for the 1 August to 15 September period of the measurements. The highest observed ozone background value was 86Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 84 ppbv measured on 8 September in east central Te xas near the Louisiana border, after several days of continuous easterly flow conditions.

Figure G1. Continuous curves give six-year (1998- 2003) average background ozone in various regions in eastern Texas, smoothed with a 31-point running mean filter (Nielsen-Gammon et al., 2005). The blue (lidar data) and red line segments (TCEQ surface network data) indicate 2006 background ozone determin ations with greater emphasis on exceedance days. The widths of the line segments approximately indicate the periods considered in 2006. A characterization of synoptic fl ow based on classes of regional ozone concentrations showed higher background ozone concentrations under continen tal air compared to maritime air. Twelve sites were selected for complete ness of data coverage (>75% each year in August and September from 2001 to 2006) and regional representation (one site per county, except for two in Harris County). Each of the days includ ed in this data set was classified as “high regional ozone,” if and only if four or more stations reported maximum 8-hour-average O 3 of 75 ppbv or higher, (this included 40 days) or “low re gional ozone,” if and only if all stations reported maximum 8- hour-average O 3 of at least 40 but less th an 50 ppbv (this included 43 days). The probability distributions for the 1-hour time step points of the HYSPLIT back trajectories (72-hour, 300 meter above ground level/mid-day starting point) for th e twelve sites are given in Figure G2. On “low regional ozone” days air parc el trajectories tended to cluster to the south and southeast with relatively longer fetches from the Gulf of Me xico, while on “high regional ozone” days the trajectories cluster to the northeast with relatively short fetches, indicati ng transport from within eastern Texas or from areas to the northeast of Texas.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 85 Finding G2: The net ozone flux transported out of Houston averages about a factor of two to three larger than the corresponding flux from Dallas. The fluxes from these urban areas are significant contributors to the backgr ound ozone in the eastern Texas region. Analysis: Senff et al.-NOAA, Sullivan et al.- U. Texas; Data: Senff et al.-NOAA, TCEQ.
The horizontal flux of O 3 downwind of Houston and Dallas during TexAQS 2000 and TexAQS 2006 was calculated from airborne lidar measurements of O 3 . The O 3 flux was computed by vertically integrating excess O 3 concentration in the plume (plume O 3 minus background O 3 ) between the surface and the top of the boundary la yer and horizontally between the plume edges (see Figure G3 for example of a plume cro ss section). The integrated plume excess O 3 concentration was then multiplied by the horizontal wind speed (estimated from nearby wind profilers) to yield O 3 flux, which is expressed in molecu les per second. Using data from the TexAQS 2000 and 2006 studies, fluxes were comput ed for the Houston area on five days (8/28 and 9/06/2000; 8/12, 8/14, and 8/30/2006) and the Dallas/Fort Worth area on one day (9/13/2006). The ozone flux from Houston ranged from 3.2 * 10 26 to 6.0 * 10 26 molec s -1 , and - within the uncertainties - was similar for the 2000 and 2006 cases. The average ozone flux from Houston of 4.6 * 10 26 molec s -1 , emitted over the 8-hour photochemically active part of one day, is sufficient to produce a 10-ppbv increase in oz one over an approximately 10,000 square mile area, assuming a 2-km deep boundary layer. The flux for the one Dallas case was estimated as 1.7 * 10 26 molec s -1 , about a factor of two to three sma ller than that observed for Houston. Although this comparison is based on only one fl ux determination for Dallas, we expect this comparison to generally hold, since Dallas lacks the large ozone production from the petrochemical industry, which accounts for a large fraction of the ozone production in Houston. Figure G2 . Contour plots showing the probability distribution of origins of 72-hour HYSPLIT back trajectories ending at 12 representative TCEQ surface ozone monitoring stations in eastern Texas. Red color denotes high probability, ye llow indicates low probability. Left panel: high regional ozone days; right panel: low regional ozone days.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 86 As a complement to airborne lidar data, which are only available for rath er short time periods, a similar assessment was performed using data fr om TCEQ’s surface ozone monitoring network to estimate horizontal ozone fluxes. The horizonta l plume integral was calculated from ozone measurements at downwind surface sites. The ozone was assumed constant through the mixed layer and the vertical plume extent was estimated from mixed layer height measurements from nearby wind profilers. For the days of the lidar analysis the differen ces between lidar- and surface-network-based flux estimates were genera lly on the order of 20 to 25%, except for one case where the difference was nearly 60%. Th is one large discrepancy may be due to uncertainties in the estimation of mixing height and horizontal plume dimensions when using surface network data. The agreement between the airborne lidar and surface monitoring results suggests that routine flux assessments using surface data may be useful. Figure G3 . Time-height cross section of ozone plum e about 50 km downwind of the Houston and Ship Channel area measured with NOAA’s ai rborne ozone lidar on 8/14/2006 at about 17:00 CST. Note the strong variation of mixing height (shown as black line) across the plume.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 87 Finding G3 : Elevated background ozone concen trations for urban areas can include contributions from the recirculation of locally produced ozone or local precursor emissions. Analysis: Ryerson et al.-NOAA, Sullivan et al.-U. Texas; Data: Ryerson et al.-NOAA, TCEQ. Background ozone concentrations en tering an urban area can be elev ated by recirculation of the ozone produced in that same urban area on a previous day, or by ozone production from precursors emitted within that urban area and recirc ulated. A past example provided by TCEQ is a May 31, 2003 episode in DFW, for which O 3 concentration contours at mid-afternoon are shown in Figure G4. In this event, the one-hour-average peak O 3 was 161 ppbv, with the eighthour-average peak at 130 ppbv. The event was infl uenced by a stalled frontal passage – note the contrasting direction at the wind vanes associated with monitoring sites on the north side versus the south side of DFW. The color code for oz one concentrations in the figure is: Gray 85-99 ppbv, Orange 100-124 ppbv, Red 125-149 ppbv, Purple > 149 ppbv.

Figure G4 . (Left) Surface weather map for 6 CST 31 Ma y 2003 shows stalled front in north Texas ( http://weather.unisys.com/archive/index.html ). (Right) Mid-day contour of O 3 concentrations shows “pancake” of elevated O 3 between Dallas and Fort Worth. Wind barbs show northerly winds north of the city and southerly winds south of the city, trapping local pollution (TCEQ, 2003). A recirculation example was observed in the Ho uston area during TexAQS 2006 (Figure G5). On 26 September the Houston plume with oz one concentrations approaching 120 ppbv was carried to the southeast by the prevailing winds. On the follo wing day, winds from the south brought air from over the Gulf of Mexico into the Houston area. However, this background air contained ozone concentrations much higher (64 to 84 ppbv, average 74 ppbv) than the more characteristic concentrations of about 40 ppbv (see Figure G1). These elevated ozone concentrations were accompanied by elevated c oncentrations of other photochemical products (e.g. formaldehyde and nitric acid) and CO, a tracer of primary emissions.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 88 Results from FLEXPART partic le dispersion modeling (Figur e G6) and back-trajectories calculated from wind profiler data (not shown) both indicate th at the elevation in background ozone concentrations on 27 September were due to recirculation of the 26 September Houston area plume back into the HGB region.

Figure G5 . WP-3D flight tracks within the boundary layer in the HGB area on two successive flights color-coded and sized according to the measured O 3 concentration.

Figure G6 . Footprint emission sensitivity for air sampled by WP-3D on 27 September at the position marked by the asterisk. The integral of the product of this quantity with the surface emission density gives the expected concentration that would be measured at the aircraft position. The numerals 1 and 2 indicate days of backward transport time. The near coincidence of the asterisk and the numeral 1 indicates that emissions from 26 September have been returned to near the original emission location. The shape of the red to violet colors indicates the primary transport pathways. The color scale is logarithmic in arbitrary units.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 89 Finding G4 : Plumes from Texas urban areas make substantial contributions to the ozone, aerosol, and precursor concentrations in the rural regions of eastern Texas. Analysis: Brock et al.-NOAA, Data: Middlebrook, de Gouw et al.-NOAA. Figure G7 shows one example of model output and measurements of transported plumes of emissions and their photochemical products from th e Houston area to northeastern Texas. The model results are from the FLEXPART Lagra ngian particle dispersion model, and the measurements are from the WP-3D aircraft on 16 September 2006. There is excellent agreement between the model and the measuremen ts, with both showing that the SO 2 plume, primarily from the Parish power plant, was transported parallel and to the west of the NO x and benzene plumes, primarily from the Houston urban and Ship Channe l areas. Table G1 shows that these plumes significantly increased the total aerosol concentrations in rural northeast Texas (for reference, the NAAQS for PM 2.5 aerosol are 15.0 μg m -3 annual mean, and 35 μg m -3 24-hour mean).

Table G1 . Measured concentrations of aerosol and g as phase species in the Parish power plant and Houston area plumes in northeast Texas. Background Parish Petrochemical Industry Total mass (μg m -3 ) 4.3 7.9 5.7 Sulfate (μg m -3 ) 1.9 4.7 2.0 Organic (μg m -3 ) 1.2 1.9 2.3 Black Carbon (μg m -3 ) 0.08 0.12 0.17 Benzene (ppbv) 0.094 0.103 0.26 SO 2 (ppbv) 0.29 0.97 0.53Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 90 Figure G7 . Modeled and measured concentrations of aerosol and gaseous species in transported plumes from the Houston area to northeast Tex as. The upper panel shows the FLEXPART model calculations (Cooper et al.–NOAA) for SO 2 (primarily Parish power plant) and NO x (primarily HSC) plumes, and the lower panel shows the measurements from the WP-3D of sulfate aerosol and benzene concentrations. A plume of sulfate aerosol associated with the Parish power plant SO 2 emission plume is evident in the measurements, as is a plume of benzene associated with HSC emissions. (Lower panel covers only a fracti on of the area in the upper panel.)Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 91 Finding G5 : Dust of African origin and sulfate aerosol advected into the Houston area, under southerly flow conditions from the Gulf of Mexico, can make significant contributions to the background aero sol in the eastern Texas region. Analysis and Data: Bates and Quinn–NOAA. Figure G8 summarizes 2006 aerosol chemical composition measurements made on the Ronald H. Brown in the Houston area. The onshore sout herly flow of background aerosol (low radon concentrations indicating no contact with land fo r several days) was substantially impacted by Saharan dust and what appear to be ship emissions (acidic sulfate and nitrate). Mean (median) mass concentrations of the tota l submicrometer and supermicrome ter aerosol were 6.5 (4.6) μg

m -3 and 17.2 (8.7) μg m -3 , respectively. These mass loadings of “background” aerosol are much higher than typically observed in the marine atmosphere, and ar e large enough to substantially impact the PM loading in the Houston-Galveston area. The integrated PM 2.5 mass at ambient relative humidity (Figure G9) includes the accumu lation mode (primarily acidic sulfate and dust under southerly flow conditions, seen in Figure G8) and part of the coarse mode (primarily sea salt, dust, and the acidic nitrate and sulfate absorbed by th ese basic components seen in Figure G8). The average PM 2.5 mass advecting into the Houst on-Galveston area during TexAQS 2006 was 19 ± 11 μg m -3 (Figure G9). Aerosol composition and mass size distributions during northerly flow conditions are shown in Figures G8 and G9 for comparison with the “background” southerly flow. Air quality forecast models need to include ship emissions and dust transport to correctly char acterize aerosol loadings in SE Texas. Compliance with PM 2.5 regulations in the Houston-Galveston area may require stricter cont rols on upwind aerosol sources (e.g. ship emissions).

POM

(NH4)HSO4 Nitrate EC Seasalt

Dust Northerly Flow 20.8 μg m -3

36%

51% 8%

POM

(NH4)HSO4

Nitrate

Seasalt Dust Northerly Flow 7.4 μg m -3

9% 30% 11% 37%

13% POM (NH4)HSO4

Nitrate EC Seasalt Dust Southerly Flow Gulf of Mexico 6.5 μg m -3

61%

30% 6%

POM (NH4)HSO4 Nitrate

Seasalt

Dust Southerly Flow Gulf of Mexico 17.2 μg m -3

38%

33% 15%

7% 6% Figure G8 . Average submicrometer (top) and supermicrometer (bottom) aerosol composition in the marine boundary layer, measured (at 60% RH) from the Ronald H. Brown in TexAQS 2006 during periods of southerly and northerly flow.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 92 70

60

50

40

30

20

10

0dM / dlogDp(ambient RH)

3 4 5 6 7 8 9 10 2 2 3 4 5 6 7 8 9 10 3 2 3 4 5 6 7 8 9 10 4
D p (nm) 

### Southerly flow Northerly flow Integrated PM 2.5 Mass Southerly flow 19 ± 11 μg/m 3 Northerly flow 34 ± 20 μg/m 3

Figure G9 . Average aerosol mass size distributions at ambient relative humidity measured from the Ronald H. Brown during TexAQS 2006.

Finding G6: Nighttime chemistry influences the availability of oxides of nitrogen (NO x ), highly reactive VOC (HRVOC), and O 3 . Analysis and Data: Brown et al.-NOAA. Nocturnal measurements of key nitrog en oxide species, nitrate radical (NO 3 ) and N 2 O 5 , were made on the Ronald H. Brown and WP-3D. These compounds are important to regional air quality because their formation and subsequent reactions affect the nocturnal loss rates for NO x , O 3 , and HRVOC. Analysis of the measured NO 3 and N 2 O 5 led to the following conclusions.

• Hydrolysis of N 2 O 5 , typically the most important reac tion in the nocturnal conversion of NO x to HNO 3 , was generally inefficient in air masses sampled aloft around Houston and elsewhere in Texas. As a result, NO x emissions occurring late in the day or at night could be transported overnight in the form of N 2 O 5 to regions distant from the NO x source regions. Transport of O 3 was also efficient since N 2 O 5 is a reservoir for odd oxygen (O x ) as well. This transport may affect O 3 concentrations well downwind of major urban and industrial areas on the follo wing day (Figure G10).

• The reduced rate of N 2 O 5 hydrolysis aloft in Houst on, and the relatively warm temperatures in this area, enhanced the availability of NO 3 as an oxidant. This was important in plumes containing both NO x and HRVOC from industrial sources in Houston. Observed nocturnal loss rate s for HRVOC due to reaction with NO 3 from plumes originating in the Houston Ship channel were 0.5 – 4 ppbv hr –1 (Figure G11).

• Surface measurements of N 2 O 5 from the RHB and vertical profiling to low altitude from the WP-3D indicated more rapid loss rates for N 2 O 5 near the surface than aloft, possibly arising from concentration of sinks for NO 3 and N 2 O 5 from emissions within the shallow nocturnal boundary layer.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 93 Figure G11 . Total VOC loss rates due to reaction with NO 3 measured from the WP-3D on 11-12 October 2006. The flight track in the map is color-coded by NO 3 mixing ratio; these values are among the largest ever observed. The pie chart in sets show the relative contributions to NO 3 loss on this flight and the relative contribution of NO 3 loss to anthropogenic VOC, which is dominated by the reaction with alkenes. Apparent "Biogenic" NO 3 losses on this chart are dominated by isoprene from anthropogenic sources. Figure G10 . Fraction of NO x [F(NO x ) = (NO 3 + 2N 2 O 5 )/(NO 2 + NO 3 + 2N 2 O 5 )] present in the form of N 2 O 5 and predicted to be transported overnight from the Parish power plant near Houston to northeast Texas, based on the observed N 2 O 5 loss rates within the plume sampled in the Houston area and a forward overnight meteorological trajectory. The WP-3D flight track is color coded by F(NO x ) and the forward trajectory is color and size coded by the predicted F(NO x ) during transport. Sunrise for the trajectories occurs over east central Texas where F(NO x ) quickly drops to zero, indicating re-conversion of N 2 O 5 to NO x and O 3 in a rural area.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 94 Finding G7 : Low rural nighttime ozon e concentrations have been observed at some, but not all, rural locations in no rtheast Texas; these low nighttime ozone concentrations are not replicated in the regulatory modeling. Analysis: Sullivan et al.- U. Texas; Data: TCEQ. Photochemical models fail to reproduce the lo w nighttime ozone concentrations observed at some rural sites in northeast Texa s (Figure G12). Possible causes of this discrepancy include the presence of: 1) shallower nighttime boundary layers than predicted by the model at the affected sites, and 2) larger local NO emissions than includ ed in emission inventories. If the latter is the cause, then corrections to the emissions i nventory are needed to accurately assess NO x concentrations and atmospheric chemistry upwind of northeast Texas cities and the DFW area. Compressors at well-heads and on pipelines emit NO x , and are potential sources of local NO emissions that may be underestimated in inve ntories. The map in Figure G12 shows that northeast Texas is a region of intense natural gas exploitation. Four other monitors in that area also consistently show lowe r than expected nighttime O 3 concentrations.

KEY CITATIONS AND INFORM ATION AND DATA SOURCES Nielsen-Gammon, J.W., J. Tobin, A. McNeel, and G. Li. 2005. A Conceptual Model for EightHour Ozone Exceedances in Houston, Texas - Part I: Background Ozone Levels in Eastern Texas. HARC Report No. H012.2004.8HRA, January 29, 2005. http://www.harc.edu/Projects/AirQ uality/Projects/Projects/H012.2004.8HRA

. Figure G12 . Time series of ozone concentrations fo r 19-22 August 1999, an episode included in the DFW SIP modeling (left). Ambient data at the Cypress River site in northeast Texas are compared with modeling results. The map s hows natural gas well regions color-coded for production (right), and the arrow locates the Cypress River site.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 95 

### Response to Question H

QUESTION H Which areas within Texas adversely affect th e air quality of non-attainment areas in Texas? Which areas outside of Texas adversely affect the air quality of non-attainment areas in Texas?

BACKGROUND Question H is closely related to Question G. Here Question H focuses on the source-receptor relationships that determine the background concen trations of ozone and aerosol in Texas and the meteorologically driven tran sport processes. Question G focuses on characterizing the background concentrations and the chemical and physical processes that affect those background concentrations.

FINDINGS

Finding H1 : Ozone can be transported into th e Dallas area from the Houston area. Analysis and Data: Senff et al.-NOAA. An example of direct transport of ozone from Houston to Dallas wa s observed during TexAQS 2006 and is depicted in Figures H1 and H2. During 4–8 Sept ember 2006, there was a regional buildup of background ozone in eastern Texas, indicated by Dallas-Fort Worth (DFW) surface station ozone monitors (not shown) and airborne ozone lidar measurements (Figure H1). From 4–7 September, large-scale winds tended to be northerly to ea sterly. While the ozone was building up in Dallas, Houston also experienced a daily increase in ozone, resulting in 8-hr ozone averages up to 110 ppbv in Houston on 7 September (not shown). Between 7 and 8 September, a shift in the position of a synoptic -scale high caused the transport winds to Dallas to change from a weak northeasterly component to a stronger southerly component. This major shift in transport had a strong impact on Dallas, as 24-hour forwar d trajectories from Hous ton, beginning at 3 pm local time, indicated transport from Houston to Dallas (Figure H2). Also, elevated overnight O 3 concentrations were measured at two rural O 3 stations sited for TexAQS II (Palestine and Italy). This transport brought additional ozone to a region that was already approaching 8-hr exceedance levels, resulting in 3 stations exceeding the 80 ppbv 8-hr-average O 3 in the DFW network.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 96 Figure H1 . Airborne ozone lidar measurements on 4 and 8 September 2006, showing the large increase in background ozone in eastern Texas between the two days.

Figure H2 . Forward trajectories starting at 3 pm local time in Houston, 7 September, and ending at 3 pm local time on 8 September 2006. The trajectories show direct transport from Houston to Dallas. 7 September was an exceedance day in Houston; 8 September was an exceedance day in Dallas. (The trajectory map was created using the NOAA Physical Science Division (PSD) upper air back-trajectory tool [ http://www.etl.noaa.gov/programs/2 006/texaqs/traj/ ]).Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 97 Finding H2 : High ozone concentrations in eastern Texas result from both in-state sources and transport of continental air from the east and northeast. Analysis: Sullivan et al.-U. Texas. Ensembles of historic HYSPLIT (Draxler and Rolph, 2003; Rolph, 2003) 5-da y back trajectories have been run from major Texas cities on high ( ≥ 75 ppbv 8-hr max) and low O 3 days to characterize the upwind “dirty” a nd “clean” typical fetches. A re sidence time analysis of the “dirty” fetch trajectories yields high O 3 air residence maps for northeast Texas, as exemplified in Figure H3. These maps (particula rly the right one) show that transport of air from the east and northeast support many high O 3 episodes in August through October, but this analysis also shows that on high O 3 days in eastern Texas, the upwind air is resident within the state for several days, potentially building up background ozone fr om in-state source emissions.

Using a new statistical analysis tool in HYSPLIT, an ensemble of back-trajectories can be categorized into clusters of sim ilar behavior. Back-trajectories from East Texas from August and September 1997–2006 cluster into 5 categories: “s hort fetch” within Texas, Arkansas, and Louisiana; “E-SE” running through the southeaste rn US and the northern Gulf of Mexico; “SSE” running through the Gulf to the Yucatan; “NE” running to the Midwest; and “north” running through the north-central US. An assessment of back-trajectory cl usters by year compared with regional O 3 levels for August and September 1997–2006 s hows that the “NE” cluster is best associated with high regional ozone, with the “sho rt fetch” having the se cond highest correlation. There is some evidence that the year-to-year va riation in the distribut ion of back-trajectory clusters helps explain th e relative severity of O 3 seasons. Figure H3 . Contouring of HYSPLIT 500 meter 5-day back -trajectories on moderate and worse O 3 days in San Antonio (left) and Tyler/Longview/M arshall (right), color coded by density of end point frequency.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 98 Finding H3 : A synthesis of satellite and in si tu measurements with photochemical modeling and Lagrangian trajectory analys es provides a quantific ation of regional influences and distant sources on Houston and Dallas air quality during TexAQS 2006. Analysis: Pierce et al.–NASA; Data: M ODIS, AIRS, CALIPSO, and TES teams–NASA, NOAA/NESDIS, UMBC. Realtime Air Quality Modeling System (RAQMS) ( http://rossby.larc.nasa.gov/RAQMS/ accessed October 2006) chemical analyses provide a means to quantify regional influences and distant sources on local air quality in Texas. Total column ozone from the Ozone Monitoring Instrument (OMI) and ozone and carbon monoxi de profiles from the Tropospheric Emission Spectrometer (TES) onboard the NASA Aura satellite constrain these analyses, and fire counts from the Moderate Resolution Imaging Spectro radiometer (MODIS) instrument onboard the NASA Terra and Aqua satellites are utilized to generate biomass burning emissions. The fidelity of the RAQMS ozone analysis for the Tex AQS 2006 period was assessed with independent satellite observations and aircraft data. The resulting bias-corrected RAQMS chemical analyses were used to provide estimates of backgr ound composition along ensemble back trajectories initialized daily at 18Z from surface EPA AI RNow ozone monitoring stations within the Houston and Dallas metropolitan statistical ar eas (MSA). Lagrangian averaged ozone production minus loss (O 3 P-L) rates along the back trajectories were used as a metric to classify back trajectories. The Lagrangian averages we re computed during time periods when the back trajectories were outside the respec tive MSA, defined as more than 2 o in longitude or latitude away from central Houston or Dallas. Three trajectory classes were considered: Class 1 (O 3 P-L > 10ppbv/day); Class 2 (0 < O 3 P-L < 10ppbv/day); and Class 3 (O 3 P-L < 0ppbv/day). A time series depiction of the RAQMS back-tra jectory analysis of regional influences on Houston and Dallas O 3 is shown in the upper panels of Figure H4. The red line shows the observed mean and variability of surface O 3 measurements in the urban area at 18 UTC extracted from EPA’s AIRNow data system ( http://www.airnow.gov/ accessed October 2006). The blue lines show the RAQMS chemical analysis with the solid blue for the AIRNow mean (biascorrected), and the dashed blue for the backgr ound mean (bias- corrected) immediately prior to entering the urban area. The co lor bar along the upper part of each time series indicates the regional influence classification co de for the modeled extent of O 3 production and loss upwind (see key). The Lagrangian analysis shows that periods of enhanced (P-L >10ppbv) regional O 3 production preceded 6 out of 9 Houston periods a nd 7 out of 15 Dallas periods with elevated O 3 (greater than 60 ppbv) during the 15 July – 15 Oc tober 2006 TexAQS II period. Maps of source regions for days with enhanced regional O 3 production are shown in the lower panels of Figure H4. Houston enhanced regional O 3 production events have a Midw est/Ohio River Valley source with significant O 3 P-L (40ppbv/ day) due to NO x sources along the southern Great Lakes. Dallas enhanced regional O 3 production events have a broad Great Plains/Midwest/Ohio River Valley source with significant O 3 P-L (30ppbv/ day) due to Chicago NO x sources along the southern Great Lakes.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 99 Case studies were conducted to investigate the processes resp onsible for enhanced regional O 3 production rates during the TexAQS II period. On e such case is early September 2006. Twelveday boundary layer back trajectories were ini tialized at locations where the High Spectral Resolution Lidar (HSRL) instrument (used to look down from the NASA King Air airplane to = Net O 3 Loss

=0< O 3 P-L <10 ppb/day

= O 3 P-L >10 ppb/day Trajectory Classification

Figure H4 . Lagrangian analysis of regional influences on Houston/Dallas O 3 . Left, time series of Houston and Dallas observed, analyzed, and background ozone. Color bar along upper part of each time series indicates regional influence trajectory classification. Below, maps of Houston and Dallas Class 1 (see text) NO y source contributions (ppbv/day). Red =AIRNow MSA mean+Std Blue =RAQMS MSA (solid) Regional (dashed) bias corrected mean

Dallas HoustonFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 100 assess the aerosol loading of the vertical column of air below) observed enhanced aerosol optical depths (AOD) indicating the presen ce of elevated particulate matter concentrations in the vicinity of Houston on 4 September 2006. Based on calculati ons of regional 24-hour rolling averages of TCEQ’s hourly PM 2.5 concentrations, 4 September was one of only three days in the TexAQS 2006 period with widespread rolli ng 24-hour concentrations above 20 μ g m -3 . In addition, a speciation monitoring site in Deer Park showed elevated sulfate and carbon material for that date. The back trajectories link the local 4 Se ptember HSRL measurements to satellite column and profile measurements from various sensors on low-earth orbit satell ites (see Table H1 and Figure H5) on 23 August. The 12-day back trajectories i ndicated two primary source regions: southern Canada and the eastern US. The MODIS sensor and AIRS sensor show column AOD and carbon monoxide (CO) enhancements associated with Pacific Nort hwest wild fire emission s within the Canadian branch, and CO enhancements associated within th e eastern US branch of the back trajectories. CALIPSO attenuated aerosol backsc atter cross-sections through the central US show an elevated aerosol layer associated with wild fire emi ssions and boundary layer aerosol enhancements over the eastern US. TES CO vertical cross-sections, which follow th e same orbit as CALIPSO, show both lower and upper tropospheric CO enhancements. This case study illust rates the influence of remote emissions from the southeastern US and Pacific Northwest on Houston air quality. This analysis underscores the importance of integrating satellite, aircraft, and surface measurements of aerosol and trace gases in conjunction with advanced modeling techniques, for characterizing the impact of emissions from remote sources on local Texas air quality. Additional case studies are presented on the TCEQ website ( http://www.tceq.state.tx.us ), and a detailed case study is available from the 29-31 May Pr incipal Findings Data Analysis Workshop for a 20 July 2004 O 3 and PM 2.5 episode in the Dallas area.

Table H1 . Satellite sensors and resulting data products utilized for 4 September 2006 case study.

NASA Sensor Product MODIS - Moderate Resolution Imaging Spectro-radiometer Aerosol optical depth CALIPSO - Cloud-Aerosol Lidar and In frared Pathfinder Satellite Observation Aerosol optical depth profile TES - Tropospheric Emission Spectrometer Carbon monoxide profile estimate AIRS - Atmospheric Infrared Sounder Carbon monoxide columnFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 101 Yellow here at 30 deg. latitude is suspected sulfate from eastern U.S. Here, aloft at 45 deg. latitude, is suspected Canadian fire aerosol.

Here also at 45-50 deg. latitude is elevated CO, believed to be from fire. Figure H5

. MODIS and AIRS satellite

sensor observations, with CALIPSO and TES orbits given by white lines. Center CALIPSO/TES ground track in two top panels represents 8/23/06 orbit,with colorcoding for AOD (left) and CO (right).

Bottom two panels show corresponding vertical profiles along the center ground tracks, with x-axis roughly the north-south direction.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 102 Finding H4 : In the Dallas area, local emission s and transport each contributed about equally to the average 8-hr ozone exceedance in 2002. Transported ozone alone can bring the Dallas area close to exceeding the 8-hour ozone standard. Analysis: Kemball-Cook et al. –ENVIRON; Parrish et al.-NOAA. Data: Ryerson et al.–NOAA. Transport contributions to Dallas area O 3 were quantified for June–September 2002 using the CAMx photochemical model with emissions fr om EPA’s 2002 National Emissions Inventory (NEI) (updated by the Central Regional Air Pl anning (CENRAP) consortium) and meteorology from the MM5 model (Kemball-Cook et al., 2006) . The Dallas area had 35 days in 2002 with monitored 8-hour ozone levels of 85 ppbv or high er (Figure H6). Averaged over these days, Dallas area emissions contributed about 48 ppbv a nd other sources 54 ppbv to the total modeled ozone 8-hour maximum of 102 ppbv on the aver age exceedance day. The modeled average transport contribution from other parts of Texas was 6 ppbv, and there were days when northeast Texas, Houston/Beaumont, south Texas, and cen tral Texas individually contributed 9 ppbv or more. The average modeled transport contribu tion from other US states was 28 ppbv, and the largest contributing states were Louisiana, Ar kansas, Mississippi, Alabama, and Tennessee. These findings are consistent with back trajectory and residence time analyses. The example shown in Figure H7 compares CAMx O 3 transport contributions to back trajectories for 7 August 2002. The 5-day back trajectories cross NE Texa s, Arkansas, and Tennessee, extending to the mid-Atlantic region; the CAMx modeling finds these same regi ons contributing to the Dallas area O 3 exceedance at the Ft. Worth NW monitor on 7 August 2002.

Estimates of the O 3 imported into the DFW area from aircra ft data are in general agreement with the trend from the CAMx model, although the ai rcraft data suggest that the transported background ozone contributed an even larger fr action of the ozone in the DFW region than suggested by the model calculations. The Elect ra aircraft in Tex AQS 2000 and the WP-3D aircraft in TexAQS 2006 measured O 3 upwind, across, and downwind of Dallas on two days during each field study. The green symbols in Figure H6 show the results from the 23 August and 7 September 2000 flights, and the orange sy mbols the results from the 13 and 25 September 2006 flights. The peak 8-hour-average O 3 for these four flights are from the monitoring data, and the DFW contribution is estimated from the difference between these peak values and the background O 3 measured upwind of DFW. 23 August 2000 is the one day investigated by the aircraft flights that had a significant exceedan ce of 98 ppbv. The background ozone transported into Dallas was about 72 ppbv, or 73% of the total, on that day. On some days transported ozone can bring the Dallas area close to an 8-hour exceedance without any added contribution from local emissions.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 103 A ugust 7, 2002--106 ppb at Fort Worth NW (CAMS 13), 95 ppb in CAMx

0 5 10 15 20 25 30 35 40DFW NNANorthTexasArkansasAlabamaTennesseeMid AtlanticStatesGeorgiaMississippiBiogenicsBoundaryConditionsOzone Contribution (ppb)

Figure H7 . Modeled contributions to one monitor’s ozone total concentration. Local is largest single contributor, but transport from upwind source areas significantly increase concentrations (analysis by Kemball-Cook et al., 2006)

. Trajectories from 3 different altitudes over Ft. Worth to characterize upwind areas for the column of air 2 pm CST on 7 Au g us t 2002. Figure H6 . Measured peak 8-houraverage O 3 in the DFW area as a function of the DFW contribution to that peak. The DFW contribution is derived from the difference between the measured peak from the regional monitoring network, and the background ozone transported into the DFW region. The small symbols are from the model results from the CAMx model for each day 1 June - 30 September 2002. The larger symbols are from aircraft transects upwind and across the DFW region on four days in 2000 and 2006.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 104 Finding H5 : In the Houston area, local emission s and transport each contributed about equally to the average 8-hr ozone exceedances investigated by aircr aft flights in 2000 and 2006. As in Dallas, transported ozone alone ca n bring the Houston area close to exceeding the 8-hour ozone standard. Analysis Parrish et al.-NOAA; Data: Ryerson et al.–NOAA.
Houston area emissions contri buted about 48 ppbv and transpor ted ozone about 59 ppbv to the average total ozone 8-hour maximum of 107 ppb on the seven exceedance days investigated by aircraft measurements during 2000 and 2006. Figure H8 shows the apportionment of the measured maximum 8-hour-average ozone for all days characterized by aircraft measurements. The linear least-square fits give slopes near unity for the separate years, and for the total data sets, which indicates that the local and tran sported background contributions are largely independent of each other. The background contribution was similar between the two years, but the local contribution was considerably larger in 2000 than 2006. The transported background ozone reached a maximum of 70 ppbv, which can l ead to an exceedance with a relatively small additional contribution from local sources.

140

120

100

80

60

40

20

0HGB Maximum 8-hr Average O3(ppbv)

80 60 40 20 0
Transported Ozone Background (ppbv) y = (0.98 ± 0.19)x + 28 ± 10 r 2 = 0.80 y = (0.77 ± 0.66)x + 57 ± 35 r 2 = 0.19 y = (1.00 ± 0.30)x + 35 ± 16

R

2 = 0.42

80 60 40 20 0
HGB Ozone Contribution (ppbv) y = (1.00 ± 0.22)x + 50 ± 9

R

2 = 0.58

y = 0.91x +51 r 2 = 0.17 y = 0.92x +56 r 2 = 0.69 HGB TexAQS 2006 TexAQS 2000

Figure H6 . Measured peak 8-hour-average O 3 in the HGB area as a function of the background ozone transported into the region (left) and the local HGB contribution to that peak (right). The background ozone was determined from aircraft transects upwind and across the HGB region on seventeen days in 2000 and 2006. The HGB contribution is derived from the difference between the measured peak and the background.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 105 KEY CITATIONS AND INFORM ATION AND DATA SOURCES Draxler, R.R. and Rolph, G.D. 2003. HYSPLIT ( HYbrid Single-Particle La grangian Integrated Trajectory) Model access via NOAA ARL READY Website ( http://www.arl.noaa.gov/ready/hysplit4.html ). NOAA Air Resources Laboratory, Silver Spring, MD. Kemball-Cook, S., M. Jimenez, and G. Yarw ood. 2006. Seasonal Model of Northeast Texas Ozone for Summer 2002. http://www.netac.org/ 0405closeout/Task_6.1.pdf (accessed July 6, 2007). Rolph, G.D. 2003. Real-time Environmental A pplications and Display sYstem (READY) Website ( http://www.arl.noaa.gov/ready/hysplit4.html ). NOAA Air Resources Laboratory, Silver Spring, MD.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 106 

### Response to Question I

QUESTION I Why does the SAPRC chemical mechanism give different results than the carbon bond (CB-IV) mechanism? Which replicates the actu al chemistry better?

BACKGROUND Gridded, regional photochemical models, used in developing State Implementation Plans (SIPs), use simplified photochemical reaction mechanis ms. The two mechanisms that are most commonly used are the [California] Statew ide Air Pollution Research Center (SAPRC) mechanism and the Carbon Bond (CB) mechanism. Both mechanisms are approved for use by the US EPA and are updated periodically to inco rporate new experimental findings. For most urban areas, the CB mechanism, version IV (CB-IV) and the 1999 SAPRC (SAPRC99) mechanism yield similar results, but for cond itions found in Houston, the SAPRC99 mechanism leads to concentrations of ozone that are up to 30-50 ppbv greater than in CB-IV and is more sensitive to reductions in NO x emissions, especially on days with high predicted ozone concentrations (Figure I1). These differences in the sensitivity of chemical mechanisms to emission reductions could have significant c onsequences for determining the magnitude of decreases in emissions of ozone precursors that will be required to demonstrate attainment of the current 8-hour, 80 ppbv NAAQS for ozone.

Figure I1 . Predictions of domain-wide maximum O 3 concentrations in Comprehensive Air Quality Model, with extensions (CAMx) on 30 August 2000. (Faraji et al., 2007a)Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 107 FINDINGS

Finding I1 : Air quality modeling for both 2000 and 2006 shows substantial differences in ozone concentrations predicted by the SAPRC99 and CB-IV chemical mechanisms. Analysis: Faraji et al. (2007a,b); Byun-U. Houston. Simulations performed using multiple regional photochemical modeling packages (Comprehensive Air Quality Model, with exte nsions [CAMx] and Community Multi-scale Air Quality model [CMAQ]), and multiple emissions preprocessing systems (Sparse Matrix Operator Kernel Emissions [SMOKE] and Emissions Pr eprocessing System version 2 [EPS2]) for 2000 and for 2006 predicted higher ozone concentratio ns when the SAPRC99 mechanism was used than when the CB-IV mechanism was used. The differences in predicted ozone concentrations were greatest when predicted oz one concentrations were high.

Finding I2 : In regions with very high VOC reactivity and high NO x emission density, differences in ozone formation and accumula tion predicted by regional photochemical models using the SAPRC99 and CB-IV mechan isms are due to differences in: (1) the chemistry of aromatics, especi ally mono-substituted aromatics (e.g., toluene), (2) nitric acid formation rates, and (3) the rates of free radical source terms in the SAPRC and CB-IV mechanisms. Analysis: Faraji et al., 2007a. In reactions of mono-substituted aromatics, th e CB-IV mechanism predicts a higher proportion of ring-retaining products, such as cresols, th an SAPRC99. These ring -retaining products are less reactive than the ring opening products. Th e CB-IV mechanism also predicts: (1) more extensive nitric acid formation, (2) less extens ive formation of peroxya cetyl nitrate (PAN), and (3) decreased formation of free radicals than the SAPRC99 mechanism. The SAPRC mechanism also has several source reactions for higher al dehydes that are not present in CB-IV. The enhanced free radical production in SAPRC99, as compared to CB -IV, leads to accumulation of higher concentrations of radicals.

Finding I3 : The differences in th e predictions of the SAPRC99 and CB-IV mechanisms can be probed using simulations of model co mpounds and comparisons of the simulations to environmental chamber data. Fo r simulations involving CO and NO x (inorganic chemistry), the predictions of the CB-IV a nd SAPRC99 mechanisms that are used in regional photochemical models (with no ch amber wall corrections) converge if the rate constants for the OH + NO 2 reaction are made consistent between the two mechanisms. The predictions of the Carbon Bond mechanis m, version 5 (CB05) also converge to the same predicted values if the OH + NO 2 rate constant is made consistent with the other mechanisms. Analysis: Faraji et al., 2007b. Equating the rate constant for the OH + NO 2 reaction in CB-IV, CB05, and SAPRC99 mechanisms, in the form in which they are us ed in regional photochemical modeling, led to convergence in the concentrations of O 3 , OH, HO 2 , NO, and NO 2 . This suggests that in photochemical modeling simulations, the major differences in the inorganic chemistry between the mechanisms are due to differences in the rate parameters of the OH + NO 2 reaction.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 108 However, CO - NO x experiments may not be sensitive to all aspects of inorganic mechanisms that affect simulations under ambient conditions.

Finding I4 : The performance of the mechanisms in simulating olefins chemistry can be improved through more explicit representation of internal olefin chemistry, which has been added in CB05. In addition, performance of the SAPRC99 mechanism in simulating chamber data was improved for some experime nts when propene was modeled explicitly, as opposed to being represented by a lumped chemical species.

Finding I5 : For high reactivity chamber experiment s involving olefins, sensitivity analyses indicated that mechanism adjustments that wo uld lead to increased radical concentrations (increasing the radical yield in olefin -ozone reactions and changing the OH+NO 2 termination rate constant) had little impact on predicted ozone concentrations.

Finding I6 : The SAPRC99 mechanism performed better than the CB mechanisms in simulating some chamber exp eriments with toluene; the mechanism performances were more comparable for xylenes and other multiply substituted aromatics.

Finding I7: The differences between the SAPRC and CB-IV mechanism predictions for toluene chemistry decrease substantially if th e yield for the lumped species CRES, and rate constant for the OH + NO 2 , are made consistent between the two mechanisms. The predictions of the CB05 mechanism also conver ge to the same predicted values if the CRES yield and the OH + NO 2 rate constant are made consistent with the other mechanisms. Analysis and Data: Faraji et al., 2007b. The lumped group of chemical species, CRES, as used in the photochemical reaction mechanisms, is often referred to as cresol, and comparisons between chamber experiments involving aromatics and observed cresol concentr ations are generally better for the SAPRC mechanism than for the CB mechanisms. Howeve r, in the CB mechanism, the CRES species is used to represent both cresol yields and other products (such as unsatur ated dicarbonyls) that react rapidly with the nitrate radi cal. Therefore direct comparisons of CRES yields with cresol concentrations for the CB mechanisms are not appropr iate. It is clear that there are substantial differences in predictions of aromatics chemis try between the CB and SAPRC mechanisms, that the toluene mechanisms all need to be revised to improve their consiste ncy with chamber data, and that much still remains unres olved about aromatics chemistry.

Finding I8: For simulations of ambient surrogate mixture experiments in the UCR EPA chamber, all mechanisms underpredict O 3 at low ROG/NO x ratios, with the bias decreasing as the ROG/NO x ratio increases. In simulations of mixture experiments in chambers without aromatics, CB05 performs the best, and with no dependence of bias on the ROG/NO x ratio. Analysis and Data: Faraji et al., 2007b. Figure I2 shows the results of comparisons of modeled simulations of environmental chamber experiments performed at University of Calif ornia, Riverside (UCR). The performance of SAPRC99 in simulating the UCR EPA mixture expe riments (both with a nd without aromatics) improved considerably if compounds are modeled explicitly, rather than in lumped groups.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 109 Full Surrogate - Arc Lights Full Surrogate - Blacklights

ROG/NO x Ratio ROG/NO x Ratio

Non-Aromatic Surrogate - Blacklights

ROG/NO x RatioΔ([O3]-[NO]) Model Error

Note: Full surrogate experiments include experiments with formaldehyde removed. -90% -75% -60% -45% -30% -15%

0% 15% 30%

1 10 100 1000 -90% -75% -60% -45% -30% -15%

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-90% -75% -60% -45% -30% -15%

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1 10 100 1000 Detailed SAPRC-99 CAMx SAPRC-99 CB05 CB4

Figure I2 . Comparison of CB-IV, CB05, and SAPRC99 m odel errors for UCR EPA surrogate runs. (Faraji et al., 2007b)

Findings I3-I8 were arrived at by analyzing model compound simulations and environmental chamber data. These six findings are consistent with Findings I1 and I2, which are based on sensitivity analyses performed using the regi onal photochemical models. Additional analyses were performed using ambient data from Housto n collected during a stagnation event with high olefin concentrations.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 110 Finding I9 : SAPRC99, CB-IV, and CB05 all successful ly predicted concentrations of ozone and other species during simulation of a stagnati on event, if the chemical mechanisms were initialized after initial high co ncentrations of olefins had reacted. None of the mechanisms successfully predicted a rapid rise in radi cal concentrations and ozone concentration concurrent with initially high C2, C3, and CM BO concentrations during the event. Analysis and Data: Faraji et al., 2007b. Analyses have been performed on ambient data collected during TexAQS 2000 at the heavily instrumented LaPorte site (Faraji et al., 2007b) . Specifically, a period of stagnation at the LaPorte airport was simulated. This stagnation period had high concentrations of C2 and C3 alkanes and alkenes, as well as high concentrations of a marker of chlorine chemistry (CMBO – 1-chloro-3-methyl-3butene-2-one) th at lasted for approximately one hour. The data suggest that a source of radicals, not accounted for in the current mechanisms was present, but no definitive source of the radicals was identified.

RECOMMENDATIONS Model compound analyses and chamber data, as well as sensitivity analyses in CAMx, indicate that major causes for the differences in SAPRC and CB mechanisms under conditions encountered in eastern Texas ar e the chemistry of aromatics, and to a lesser extent, radical termination rates. In choosing a mechanism that will be most effective for eastern Texas, several guiding principles emerge.

1. Over the past several decades, comprehens ive updates to the CB and SAPRC chemical mechanisms have been released roughly every 5-10 years. These updates incorporate changes to the mechanisms that reflect e volving knowledge about ra te constants and key chemical pathways. For example, the rate parameters for the critical OH + NO 2 rate constant have been adjusted in CB05, as compared to CB-IV, and in SAPRC07, as compared to SAPRC99. In addition, the update s often provide explic it representations of chemical species that have recently been id entified as being especially important. For example, explicit isoprene chemistry was incorporated into both SAPRC and CB mechanisms in the 1990s and the newly released CB05 incorporates internal olefins as an explicit compound class. These updates genera lly reflect more current knowledge, and, as a guiding principle, should be used when possible.

2. Recognizing that it may be difficult to una mbiguously define the most appropriate mechanism for use in Houston, the effectivene ss of proposed control st rategies should be evaluated using multiple chemical mechanisms.

3. As new comprehensive updates to the CB a nd SAPRC mechanisms are developed, more explicit representation of both key chemical pathways (e.g., the low NO x routes for aromatics) and key chemical species (prope ne, toluene, and possibly other aromatics) would likely make the mechanisms more us eful in analyzing the complex industrial emission sources in the Houston area.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 111 KEY CITATIONS AND INFORM ATION AND DATA SOURCES Carter, W.P.L. 2004. Evaluation of a Gas Phas e Atmospheric Reaction Mechanism for Low NO x Conditions. Final Report to the California Air Resources Boar d, Contract No. 01-305. http://pah.cert.ucr.e du/~carter/bycarter.htm

. Faraji, M., Y. Kimura, E. McDonald-Buller, and D. Allen. 2007a. Comparison of the Carbon Bond and SAPRC photochemical mechanisms under conditions relevant to southeast Texas. Atmospheric Environment , in press. Faraji, M., G. Heo, Y. Kimura, E. McDonald-Bu ller, D. Allen, G. Yarwood, G. Whitten, and W. Carter. 2007b. Comparison of the Carbon B ond and SAPRC photochemical mechanisms. Report to the Texas Commission on E nvironmental Quality, August, 2007. Yarwood, G., S. Rao, M. Yocke, and G.Z. Whitten. 2005. Updates to the Carbon Bond Mechanism: CB05. Report to the U.S. E nvironmental Protection Agency (December). www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf

.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 112 Response to Question J

QUESTION J How well do air quality forecast models predict the observed ozone and aerosol formation? What are the implications for improvement of ozone forecasts?

BACKGROUND An assessment of seven ai r quality forecast models (AQFMs) operating in real time during TexAQS 2006 focused on skill at predicting maximum 8-hour-average O 3 at 119 sites and 24-hr-average PM 2.5 levels at 38 sites in eastern Texas, western Louisiana, Arkansas andOklahoma. These models included: the NCEP CMAQWRF model (12-km horizontal resolution), two versions of the NOAA/ESRL WRF-Chem model (12- and 36-km res.), the Canadian CMC AURAMS (28-km) and CHRONOS (21-km res.) mode ls, one version of the Baron-AMS MAQSIP-RT model (15-km res.), and the University of Iowa STEM model (12-km res.). Details on each of these modeling systems can be found in McKeen et al. (2005), and internet web links listed below. Three standard st atistical parameters, Mean Bias, Root Mean Square E rror (RMSE), and correlation coefficients were evaluated in this preliminary wo rk. Skill for these three statistics is measured relative to a persistence forecas t, or the forecast that tomorrow’s AQ levels are the same as today’s observed levels. In addi tion, bias-corrected model forecast values are calculated based on the mean O 3 or PM 2.5 bias at each site at each hour of the day, averaged over the previous 7 days. The statistical pa rameters are also calculated from the ensemble of the models, and the ensemble of bias-corrected models. The summary statistics for the models and their ensemble are shown in Fig. J1 for O 3 and Figure J2 for PM 2.5 . Also shown are the RMSE for persistence and climatology forecasts, and correlation coefficien ts for persistence. Categorical statistics (measures of how well a model predicts an event) related to the predictions of daily maximum 8- hour-average O 3 levels exceeding the 85 ppbv regulatory threshold are also summarized for the seven models, their mean ensemble, and with two bias-correction options (Figures J3 and J4). The four categorical statistics ev aluated in this work were: Pr obability of Detection, False Alarm Rate, Critical Success Index, and Bias Ratio. The forecasts of roughly 25 chemical, aerosol, meteorological, and radiation variables from the seven models have been compared with data collected on board the WP-3D aircraft for individual transects and vertical profiles on a flight-by-fl ight basis for the first 12 flight legs of the aircraft deployment. These comparisons are available for viewing at the publicly available NOAA web page, http://www.esrl.noaa.gov/csd/2006/modeleval/. Concentrations and fluxes of key atmospheric constituents upwind and downw ind of Houston and Dallas have also been compared, allowing assessments of model reliability in terms of emissions inventories, and efficiencies of O 3 and PM 2.5 formation from these large urban sources. The University of Houston also made nine AQ forecasts using three model resolutions and three emission scenarios with the MM5/CMAQ model. Detailed statistical summaries for O 3 and Air Quality Forecast ModelsAURAMS – A Unified Regional AirQuality Modeling System MAQSIP-RT – Multiscale Air Quality Simulation Platform-Real Time CHRONOS – Canadian Hemispheric and Regional Ozone and NO x SystemCMAQ5x/WRF – Community Multiscale Air Quality model/Weather Research Forecast model University of Iowa STEM – Sulfur Transport and Emissions Model
WRF/Chem – Weather Research Forecast model/Chemistry versionFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 113 PM 2.5, for each forecast, and for each of the CA MS surface monitors can be found at the University of Houston web page, http://www.imaqs.uh.edu/ftp/AQF_usa/ (password protected).

Figure J1 . Summary statistics for 8-hour maximum ozone for nine air quality forecast models and their ensemble mean and bias-corrected ensemble mean.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 114 Figure J2 . Summary statistics for 24-hour-average PM 2.5 for seven air quality forecast models and their ensemble mean and bias-corrected ensemble mean.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 115 FINDINGS

Finding J1a : Most forecast models exhibit skill in predicting maximum 8-hr-average O 3 but none of the models is better than p ersistence in predicting 24-hr-average PM 2.5 levels. Analysis: Wilczak et al.-NOAA. Model Data : McQueen et al.-NOAA/NWS, Grell et al. NOAA/GSD, Carmichael et al.-U. Iowa, McHenry et al.-Ba ron AMS, Gong et al.-Met. Srv. Canada, Bouchet et al.-Met. Srv. Canada.
When all O 3 data are considered:

– Nearly all models beat persistence RMSE and r-coefficients wit hout bias correction.

– The ensemble performed better than all i ndividual models, and the bias-corrected ensemble showed the best statistical performance.

– Bias correction improved RMSE scores apprec iably and r-coefficients to some degree.

When all PM 2.5 data are considered:

– In contrast to O 3 , no model or ensemble, or bias correction, beat persistence.

– All but one model was biased low, and bias correction improved r-coefficients significantly.

– Since they are not included in the models, the occurrence of Saha ran dust events during August and September influenced the bulk PM 2.5 statistics. Removal of these events from the statistical evaluations is an other required step in model analysis.

Figure J3 . Categorical statistics related to the 85 ppbv exceedance for 8-hour maximum ozone for seven air quality forecast models and their ensemble.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 116 Finding J1b : Seven air quality forecast models a nd their ensemble were generally unable to forecast 85 ppbv 8-hr-average O 3 exceedances with any reliability. Analysis: McKeen et al.-NOAA. Model Da ta: McQueen et al.-NOAA/NWS, Grell et al. NOAA/GSD, Carmichael et al.-U. Iowa, McHenry et al.-Ba ron AMS, Gong et al.-Met. Srv. Canada, Bouchet et al.-Met. Srv. Canada.

– Although the 7-model ensemble had the best ove rall bulk statistics, and was biased high by 5 ppbv, its ability to predict an exceed ance event was significantly worse than persistence.

– The ensemble, like most of the models, predic ts far fewer events, and missed most of the exceedance events in the Houston area (see Figure J4). This collective model underprediction could be due to the lack of an important source component within all the models, such as highly reactive VOC emissions.

– Only one model beat persistence for both proba bility of detection and false alarm rate.

– Since most models and the ensemble are bias ed high, bias correction tended to reduce a model’s ability to predict an exceedance event even further. This was in sharp contrast to a similar categorical analysis for New E ngland during the summer of 2004 (McKeen et al., 2005), where bias corrections nearly al ways improved the critical success index.

Figure J4 . Number of times the 85 ppbv exceedance fo r 8-hour maximum ozone occurred within the reported AIRNow observations, and th e 7-model ensemble forecast.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 117 Finding J2 : Sophisticated data assimilation of meteorological and even chemical observations is essential for improving photochemical model forecasts. Analysis: Nielsen-Gammon et al.-Texas A&M U., By un et al.-U. Houston, McKeen et al.-NOAA. Model Data: Nielsen-Gammon et al.-Texas A&M U., Byun et al.-U. Houston, McQueen et al.- NOAA/NWS, Grell et al. NOAA/GS D, Carmichael et al.-U. Iow a, McHenry et al.-Baron AMS, Gong et al.-Met. Srv. Canada, B ouchet et al.-Met. Srv. Canada. Spatial and temporal accuracy of AQ forecasts are to a large degree lim ited by the accuracy of the underlying meteorological forecasts within the AQ models. Most models rely on the available NCEP/NAM model product for ini tialization and boundary conditions, which may contribute to model biases in AQ model wind fields and pr essure patterns documented throughout the field study. Retros pective forecasts and sensitivity studies are needed to assess the impact of the NAM forecasts available in the summer of 2006 to AQFMs, particularly in light of recent upgrades to th e WRF-NMM model used in the NAM forecasts. Assimilation of wind profiler data (Nielsen-G ammon et al., 2006) has been shown to improve forecast meteorology in the Houston area. Similar resear ch related to the assimilation of photochemical and aerosol data within AQFMs should be encour aged, utilizing the comp rehensive data sets from the TexAQS 2006 field study. Daytime planetary boundary layer (PBL) heights from seven of the models were compared to those derived from wind profiler measurements at several sites in east Texas. Some models showed persistent bias in daytime PBL, and no model appeared to stand out in terms of significantly better comparisons. Deficiencies in AQ forecasts from several of the models appear to be related to the formulation of PBL height a nd vertical transport within the PBL. This is shown in Figure J5, which shows a wide spread in the models’ ability to simulate water vapor and NO y profiles compared to WP-3D aircraft data for afternoon conditions on a particular clear day. It is quite common for models to exhibit unrealistic high biases in O 3 and PM 2.5 precursors downwind of source regions when PBL he ights are under-predicted. The analogous misrepresentation of the stable PBL often aff ects model-predicted offs hore pollutant transport and pollution precursor buildup from emissions along the coastlines. Persistent errors in the forecast of the low-level nocturnal jet are also characteristic of ma ny models. Preliminary sensitivity studies with the MM5/CMAQ model also have demonstrated a case of over-predicted O 3 associated with a missed forecast of widespr ead precipitation (24, 25 August). The collection of available satellite, radar, and surface network data se ts for comparing cloudiness, precipitation, and radiation with model output are needed to perform further evaluations of these parameters, and their relationships to O 3 and PM 2.5 forecasts.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 118 Figure J5 . Profiles of water vapor, relative humidity, and NO y on 25 September 06, south and downwind of Dallas. Black dots indicate observations. The horizontal transect shows high biases of NO y for those models with the lowest PBL hei g hts and reduced vertical trans p ort.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 119 Finding J3 : Model performance evaluation s and intercomparisons require a comprehensive, best-guess emi ssions inventory for the TexAQS 2006 Field Intensive. Analysis: Byun et al.-U. Houston; Data: TCEQ.
Ozone and PM 2.5 forecasts are highly dependent on the emissions inventories of precursor emissions, and PM 2.5 forecasts are also dependent on primar y emissions at many of the urban and suburban CAMS locations. A high priority in the model evaluation effort should be placed on using TexAQS 2006 field data to determine the accuracy of the inventories that drive AQ forecasts. Ozone forecasts are particularly sensit ive to emissions estimates of HRVOC, such as ethylene and propylene, from large petrochemical facilities, especially in the Houston ship channel region. Quality-assured VOC measurements from the various platforms and comparisons with AQ model results are currently in progress in order to evaluate the model emissions inventories. Two preliminary sensit ivity results from the University of Houston MM5/CMAQ model relate directly to emissions inventory validation. That model shows generally improved NO 2 and O 3 comparisons with CAMS site da ta using an emission inventory based on projections to 2005 as opposed to an inventory based on 2000 estimates. A narrow plume of extremely high O 3 observed downtown and west of Houston (7 September) that was significantly under-predicted by the original forecast was found to be replicated accurately in retrospective runs that included a VOC source in the ship channel region much larger than specified in the base emissions inventory.

KEY CITATIONS AND INFORM ATION AND DATA SOURCES McKeen, S., J. Wilczak, G. Grell, I. Djalalova, S. Peckham, E.-Y . Hsie, W. Gong, V. Bouchet, S. Menard, R. Moffet, J. McHenry, J. McQueen, Y. Tang, G.R. Carmichael, M. Pagowski, A. Chan, T. Dye, G. Frost, P. Lee, and R. Mat hur. 2005. Assessment of an ensemble of seven real-time ozone forecasts over eastern No rth America during the summer of 2004. J. Geophys. Res., vol. 110, D21307, doi:10.1029/2005JD005858. Nielsen-Gammon, J. W., R.T. McNider, W.M. Angevine, A.B. White, and K. Knupp. 2007. Mesoscale model performance with assimila tion of wind profiler data: Sensitivity to assimilation parameters and network c onfiguration. J. Geophys. Res., vol. 112, D09119, doi:10.1029/2006JD007633. Model Acronyms and Web Links AURAMS (A Unified Regional Air-Quality Modeling System) http://www.msc-smc.ec.gc.ca/research/icartt/aurams_e.html BAMS (Baron Advanced Meteorological Systems, Inc.) http://www.baronams.com/projects/SECMEP/index.html CHRONOS (Canadian Hemispheric a nd Regional Ozone and NOx System) http://www.msc-smc.ec.gc.ca/aq_smog/chronos_e.cfm CMAQ5x/WRF (Community Multi-scale Air Qua lity model/Weather Research Forecast model) http://wwwt.emc.ncep.noaa.gov/mmb/aq/ University of Iowa STEM (Sulfur Transport and Emissions Model) http://nas.cgrer.uiowa.edu/MIRAGE/mirage-2k6.html WRF/Chem (Weather Research Fore cast model/Chemistry version) http://www.wrf-model.org/WG11Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 120 

### Response to Question K

QUESTION K How can observation and modeling approaches be used for determining (i) the sensitivities of high ozone in the HGB non-attainment area to the precursor VOC and NO x emissions, and (ii) the spatial/temporal varia tion of these sensitivities?

BACKGROUND The accurate prediction of the relative response of ozone concentrations to future reductions in NO x and VOC emissions is a much sought, but very elusive goal. This prediction is central to the very important SIP-relevant question of “direction of control” – that is, should ozone control efforts in an ozone non-attainment area be focused on: a) decreasing emissions of NO x alone, b) decreasing emissions of VOC alone, or c) decreasing emissions of both NO x and VOC? In this report the closely related Questions F and K bot h deal with aspects of this issue. The Background Section of the Response to Question F discusses some issues relevant to this Question and Response; the interested reader should peruse that discussion as a prelude to this Response to Question K and also review the recently revised Guidance on the Use of Models and Other Analyses for Demonstrating Attain ment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze (USEPA, 2007). One point made in the background section of the Response to Question F is that a definitive answer to these questions requires a thoroughly tested Eulerian emissions-based air quality model that treats th e region of interest with sufficient accuracy. Another point is that this model testing shoul d involve comparisons between model results and direct observations of the relatio nships between ozone, its precursor s, and the radicals that drive these reactions. Unfortunately however, it has no t yet been possible to develop such generally applicable models because of deficiencies and uncertainties in many critical areas, including emissions, meteorological modeling, and photochemical mechanisms. In the absence of a “perfected” Eulerian air qua lity model for the HGB non-attainment area, it has been necessary to take a variety of heuristi c approaches to provide guidance to air quality managers. Heuristic implies a model that is simp lified, but that is design ed and used to learn more about some specific but important aspect of the more complex air-quality system that is to be managed. The Response to Question F discus ses the application of both observation-based and emissions-based modeling approaches for ap proximately determining the sensitivity of ozone production and concl udes that both VOC and NO x controls are presently effective in reducing the maximum observed ozone concentra tions. Continued incr emental reductions in emissions of either VOC or NO x emissions can be expected to yield incremental improvements in ozone design values in the HGB region. Here the response to Question K addresses two ve ry important questions that are related, but importantly different, in that they address how compliance with the current NAAQS for ozone can be achieved in an attainment demonstration:

• What emission controls will ultimately be necessary for the HGB area to achieve compliance with the 8-hour-ave rage, 80 ppbv ozone standard?

• What is the most effective strategy by which to reach this compliance? To address these questions, a very simple, em issions-based model was applied to the HGB region.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 121 FINDINGS

Finding K1: A simple, heuristic model based upon the Empirical Kinetic Modeling Approach (EKMA) method suggests that the HGB region may ultimately require drastic NO x emissions controls to reach comp liance with the NAAQS for ozone.

Finding K2: The same model suggests that in a proj ected future scenario with very strict VOC emission controls, but without drastic NO x emission controls, biogenic VOC emissions plus background con centrations of CO and methan e may be sufficient to cause ozone exceedances. Analysis: Dimitriades-NCSU; Luecken-USEPA. A simplified model of an inherently complex syst em can sometimes provide valuable insight into the behavior of the complex system (Dimitriade s, 2006; Dimitriades and Luecken, 2007). As a case in point, here we investigate a very simp le, Empirical Kinetic Modeling Approach (EKMA) that includes an Ozone Isopleth Plotting Research Package (OZIPR ) [see e.g. Finlayson-Pitts and Pitts, 2000]. This approach comprises a photoche mical box model, and was applied to the HGB area in order to illuminate one aspect of the co mplex chemical-transport system that defines the near-surface atmosphere in the HGB region. The model was formulated to provide an answer to the very critical question: To reach complia nce with the NAAQS ozone standard in HGB, what emission controls are necessa ry and most effective?

The model utilized an advanced version of the EKMA method fo r translating early morning (8 AM) ambient concentrations of VOC and NO x into the maximum 8-hour-average ozone concentration that accumulates by the end of the day. The model incorporates a SAPRC99 chemical mechanism and requires model and obs ervational inputs on VOC composition, sunlight intensity, diurnally varying VOC (including biogenic VOC) and NO x emissions, diurnally varying boundary layer height, a nd background ozone concentration. The results presented here were obtained for: (i) A “typical” Harris County VOC mix, based on measurements at the LaPorte site during TexAQS 2000. (ii) A constant 30-ppbv concentration of background ozone. (iii) Time-varying post-8 AM VOC and NO x emissions based on average daily anthropogenic emissions reported for Harri s county in 2002, apportioned as hourly emissions based on modeled profiles for total NO x and anthropogenic VOC emissions from EPA’s 2001 National Emissions Inventory processed using the SMOKE emissions processor ( http://www.smoke-model.org ). (iv) Time-varying post-8 AM isoprene emissions estimated from the area average isoprene emissions for Harris County from the HGB 2002 emissions inventory ( http://www.tceq.state.tx.us /assets/public/implementation/ air/sip/hgb/hgb_sip_2007/append ices/06027SIP_Appendix_F_PEI.pdf ). These emissions totaled 29 kg km -2 day -1

. (v) a typical time-varying mixi ng height based on measurements made at the Moody Tower site. The results of the model are presented in the form of a traditional EKMA diagram in Figure K1. For illustration purposes, the method was applied to 8 AM NO x and VOC concentrations measured at five sites (Clinton, Channelview, Wallisville, Lynchburg Ferry, and HRM-3) withinFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 122 Harris County during April-Oc tober of 2005 and 2006. A single ozone concentration isopleth diagram was constructed from the calculated maximum 8-hour-average ozone concentrations that accumulated in the mode l initiated with those 8 AM measured concentrations.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 8 AM anthropogenic VOC (ppmC) g pp p p

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.58 AM NOx (ppm)



## A B C

C

Figure K1 . EKMA diagram constructed for the HGB regi on. The contour lines give the maximum 8- hour-average ozone concentration (in ppbv) that accumulated starting with the morning VOC and NO x concentrations given on the respective axes. The critical, dashed line isopleth represents the maximum ozone concentration allowed in an area in compliance with the NAAQS. Figure K1 suggests that the ozone sensitivity conditions within Harris County vary from VOCsensitive to NO x -sensitive from site to site and from da y to day. For example, consider the points labeled A, B, and C in the figure. Th e two points labeled C represent non-exceedance conditions – they lie below the critical (84 ppbv) dashed line isopleth. Point A represents NO x limited exceedance conditions. Th e predicted maximum 8-hour av erage ozone concentration is above 84 ppbv, but by decreasing NO x emissions sufficiently, the 8 AM NOx concentrations will decrease so that Point A will cross the critical da shed line isopleth. Similarly, Point B represents VOC-limited exceedance conditions; by decreasing VOC emissions sufficiently, the 8 AM VOC concentrations will decrease so that Point B w ill cross the critical dash ed line isopleth.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 123 There is a critical difference in the behavior that the EKMA mode l predicts for the NO x -limited Point A and the VOC-limited Point B. Point B suggests that attainment can be most easily reached by reducing VOC emissions, but attain ment can also be reached by reducing NO x emissions if the reductions are dras tic enough. Initially at point B, NO x emission reductions alone would first increase the predicted maxi mum ozone concentration, but after a peak is reached, further NO x emission reductions would lead to a decrease in the predicted maximum ozone concentration until Point B crosses the crit ical isopleth. In contra st, VOC reductions alone applied to Point A can never bring that point below the critical isopleth, because the maximum ozone concentration is predicted to be above 84 ppbv even when the 8 AM anthropogenic VOC concentrations are zero. This model behavior occurs because biogenic VOC emissions alone (with background CO and methan e concentrations), in the presence of sufficient NO x , can produce ozone concentrations in exceedance of the ozone standard. This behavior of Point A is similar to the predictions from the Eulerian mo deling presented in Figure F3, which show that even extremely stringent VOC controls cannot decrease the HGB ozone design values enough to achieve compliance. Importantly, the EKMA model suggests that NO x -sensitive conditions as exemplified by Point A are relatively common in HGB. Of the days investigated, 54% for Clinton, 17% for Channelview, 35% for Wallisville, 39% for Lynchburg Ferry, and 43% for HRM-3 exhibited Point A-like, NO x -limited behavior. Clearly the EKMA model is highly simp lified, but it is notable that the NO x -sensitive behavior exemplified by Point A may be reasonably well re presented by the model. One of the most critical simplifications in the model is the assumption of a “typ ical” Harris County VOC mix. It is well known that the VOC composition in HGB changes markedly, from the typical urban mix of downtown Houston to the widely varying VOC emissions from the petrochemical facilities. However, the critical Point A behavior is focu sed at zero anthropogenic VOC concentration, so the assumed VOC composition will have no effect. Given the EKMA model pr ediction that drastic NO x emission controls will be required to eventually reach ozone compliance in HGB, the ex tremely difficult but central question arises: How should air quality managers in the HGB area divide ozone management resources between NO x and VOC emission controls?

Several considerations favor cont rol of VOC emissions, including:

• First, the Response to Ques tion F indicates that increm ental VOC emission reductions presently will result in incremental decrea ses in ambient ozone concentrations. Any progress in decreasing the maximum ambient ozone concentrations will have immediate health and welfare benefits.

• Second, Figure K1 does indicate that there is a modest adva ntage to VOC control when used in conjunction with NO x control. For example, if the 8 AM VOC concentration is reduced from 0.4 to 0.1 ppm C, then NO x controls need only reduce the 8 am NO x concentration to about 25 rather than about 20 ppbv. Figure F3 also indicates a similar benefit of VOC control.

• Third, VOC emissions reductions can decr ease ambient concentrations of VOC hazardous air pollutants. For example, th e ambient concentrations of formaldehyde, which in the HGB area is primarily a secondary product of HRVOC oxidation, will respond to HRVOC emission controls.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 124 Similarly, several considerat ions favor control of NO x emissions, including:

• First, NO x emissions will reduce not only locally produced ozone, but also regional background ozone. Outside of central urban ar eas and point source plumes, the regional ozone production environment is generally strongly NO x -limited.

• Second, all of the modeling approaches agree that sufficiently drastic NO x control is certain to lead to ozone attainment. Further, to the extent that the EKMA results reflect reality, such drastic control may be required to ultimately reach compliance. However, there is uncertainty whether the high degree of NO x control required for ozone attainment is socio-economically acceptable or even technologically feasible.

• Third, NO x controls will reduce local and regional levels of both nitrate aerosol, a significant cont ributor to PM 2.5 concentrations, and nitric ac id, a significant component of acid deposition. Both of th e nitrate contribution to PM 2.5 and the nitric acid contribution to acid deposition are particularly im portant issues in the western U.S. The most efficient emission control strategies will then depend upon practical and economic considerations associated with the alternatives. Given that th e Eulerian and EKMA models upon which all the preceding discussions are based are st ill imperfect, it is prudent to implement what VOC controls and/or NO x controls are presently benefici al and practical, while modeling improvements are effected.

KEY CITATIONS AND INFORM ATION AND DATA SOURCES Dimitriades, D. 2006. A Proposed Study for Determ ining the Optimum Direction of Control in the Houston-Galveston-Brazoria Ozone Non-Attain ment Area. NC State University Report to the Texas Commission on Environmental Quality. Submitted October 10, 2006. College of Natural Resources, NC State University, Raleigh, NC. Dimitriades, B. and D. Luecken. 2007. An observa tional investigation of ozone sensitivity to VOC and NOx and it variation in the Hous ton-Galveston-Brazor ia (HGB) ozone nonattainment area. Presentation at TexAQS II Principal Findings Data Analysis Workshop, 29 May-1 June 2007. Austin, TX. http://www.tceq.state.tx.us/assets/public /implementation/air/texaqs/workshop/20070529/200 7.06.01/6-Spatial+Temporal_P atterns-Dimitriades.pdf Finlayson-Pitts, B.J. and J.N. Pitts. 2000. Chem istry of the Upper and Lower Atmosphere. pp. 892-893. Academic Press, New York, NY. US Environmental Protection Agency. 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze. EPA Report 454/B-07-002. April 2007. EP A Office of Air Quality Planning and Standards, Air Quality Analys is Division, Air Quality Mode ling Group, Research Triangle Park, NC. 262 pp. http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rhguidance.pdfFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 125 APPENDIX 1. GLOSSARY OF ACRONYMS (The meaning of some of the ac ronyms more commonly used in the report are listed here.)

AIRS Atmospheric Infrared Sounder AIRS Aerometric Information Retrieval System AOD Aerosol Optical Depth AQ Air Quality AQFM Air Quality Forecast Model AURAMS A Unified Regional Air-Quality System BEIS Biogenic Emissions Inventory System BL Boundary Layer CALIPSO Cloud Aerosol Lidar and Infrar ed Pathfinder Satellite Observation CAMS Community Air Modeling System CAMx Comprehensive Air Quality Model, with extensions CB Carbon Bond CB05 Carbon Bond Photochemical Mechanism, version 5 CB-IV Carbon Bond Photochemical Mechanism, version IV CEMS Continuous Emissions Monitoring System CHRONOS Canadian Hemispheri c and Regional Ozone and NO x System CMAQ Community Multi-scale Air Quality Model CMBO 1-chloro-3-methyl-3butene-2-one CRES Lumped group of chemical sp ecies in photochemical reactions mechanisms that includes cresol CST Central Standard Time DFW Dallas-Fort Worth DIAL DIfferential Absorption Lidar EGU Electric power Generating Unit(s) eGRID Emissions and Generation Re source Integrated Database EPA US Environmental Protection Agency EPS2 Emissions Processing System version 2 EKMA Empirical Kinetic Modeling Approach HGB Houston-Galveston-Brazoria HRVOC Highly Reactive Vola tile Organic Compound(s) HSC Houston Ship Channel HSRL High Spectral Resolution Lidar HYSPLIT HYbrid Single-Particle Lagra ngian Integrated Trajectory model LPAS Laser Photo-Acoustic Spectroscopy LST Local Standard Time MAE Mean Absolute Error MAQSIP-RT Multiscale Air Qualit y SImulation Platform-Real Time MM5 Meteorological Model version 5 MOBILE6 EPA Mobile Source Emi ssion Factor Model version 6 MODIS Moderate Resolution Imaging Spectroradiometer MSA Metropolitan Statistical Area MSD Medium Speed Diesel NAAQS National Ambient Air Quality StandardsFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 126 NASA National Aeronautics and Space Administration NCAR National Center for Atmospheric Research NCEP National Centers for Environmental Prediction NEI National Emissions Inventory NOAA National Oceanic and A tmospheric Administration NOAA/ESRL NOAA Earth System Research Laboratory NOAA/NESDIS NOAA National Environmental Sa tellite Data and Information Service OMI Ozone Monitoring Instrument OZIPR Ozone Isopleth Plotting Research Package PAN PeroxyAcetyl Nitrate PBL Planetary Boundary Layer PM 2.5 Particulate Matter less than 2.5 micrometers in diameter PTR-MS Proton Transfer Reaction-Mass Spectrometer RAQMS Realtime Air Quality Modeling System ROG Reactive Organic Gases RHB research vessel Ronald H. Brown RMSE Root Mean Square Error RSST Rapid Science Synthesis Team SAPRC [California] Statewide Air Pollution Research Center SAPRC99 [California] Statewide Air Pollu tion Research Center (photochemical mechanism 1999 version) SAPRC07 [California] Statewide Air Pollu tion Research Center (photochemical mechanism 2007 version) SIP State Implementation Plan SMOKE Sparse Matrix Oper ator Kernel Emissions SOF Solar Occultation Flux SOS-OD Southern Oxidants St udy-Office of the Director SSD Slow Speed Diesel STEM Sulfur Transport and Emissions Model TCEQ Texas Commission on Environmental Quality TES Tropospheric Emission Spectrometer TexAQS 2000 First Texas Ai r Quality Study, completed during the summer of 2000 TexAQS 2006 Intensive field measurement study conducted during 11 weeks of TexAQS II TexAQS II Second Texas Air Quality Study, completed during 18 months in 2005 and 2006 TRI Toxics Release Inventory UHI Urban Heat Island UTC Coordinated Universal Time VOC Volatile Organic Compound(s) WAS Whole Air Samples WP-3D Instrumented aircraft operated by NOAA WRF/Chem Weather Research Forecast model/Chemistry versionFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 127 APPENDIX 2. FINAL REPORT FROM QUESTION L WORKING GROUP.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 128 

### Question L Final Report

QUESTION L What existing observational databases are suit able for evaluating and further developing meteorological models for application in the HGB area?

QUESTION L WORKING GROUP Leader: Lisa Darby; Particip ants: Robert Banta, John Ni elsen-Gammon, Daewon Byun, Wayne Angevine, Mark Estes, Brya n Lambeth, Stuart McKeen.

BACKGROUND In order to address this question, databases that are potentially useful to individuals performing air quality modeling for Texas, including both permanent measurements and enhanced measurements from TexAQS II deployments, we re compiled. The databases were evaluated based on several criteria, including quality c ontrol, accessibility, regional coverage, and time resolution. Web links to the databases are given, followed by a brief description and evaluation.

FINDINGS

Surface Meteorology and Chemistry Data

COOP observations http://www7.ncdc.noaa.gov/IPS/CDPubs?action=getstate Consists of monthly printed pages (available as PDFs online) containi ng station observations. No description of QA is provided on the web site.

Because these data are not in machine-readable format, they are unlikely to be useful for any systematic study. They are redundant, in the sense that the same observations should be in the normal NWS data streams.

Crop Weather Program, Texas A&M University http://cwp.tamu.edu/cgi- bin/htmlos.cgi/6742.2.1749378041063346623 The Crop Weather Program for South Texas (CWP) was developed to help farmers and consultants make management decisions conducive to profitable crop pro duction. It replaces an earlier cotton monitoring system known as the Weather Station Network Program. The CWP is the gateway for access to weather data measured by a network of 21 automated weather stations spread across 10 South Texas counties and provi des hourly measurements of air temperature, relative humidity, solar radiation, wind direction and speed, precip itation, and soil temperature at 1", 3", and 8" depths. The wind direction is re ported based on a 16-poi nt compass and the wind speed appears to be arithmetic (no vector averag e direction or speed). The wind also appears to be measured about 10 feet above ground level based on an example site photo provided (this could exacerbate exposure problems wher e buildings and/or trees are nearby).

Harris County Office of Homeland Security and Emergency Management http://www.hcoem.org/ The Harris county rainfall map site allows you to enter an amount of time (in days, hours, or minutes) before the current time, and it produ ces a map of accumulated rainfall amounts from each site, over the time requested. The data co me from 163 automatic remote sensors (part ofFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 129 the flood alert system) across the metropolitan ar ea, and they are “unofficial” (probably means not quality-controlled). The dens ity of the network allows for de tailed information regarding the horizontal distribution of the rain fall. Their locations can be found on a map link and a text link, which includes latitudes and longitudes. There is a link to an archive site where you can indicate a given amount of time before your date of interest to obtain a map of accumulated rainfall, but I could not get this part to work. If this does ev entually work, this coul d be a useful site for modelers, although it looks like the only output woul d be a map (i.e., no text dump). I suggest a following up on this site to determine if th ere is a way to order the archived data. Also on the main page for Harris County O ffice of Homeland Security and Emergency Management is a link to a real time Houst on speed map. Along the outlines of the major highways the current speed of traffic is show n in color (indicating speeds <20, 20-29, 30-39, 40- 49, and 50+ MPH, or no data). On this site is a link to the Houston speed map archives ( http://traffic.houstontranstar.or g/map_archive/map_archive.aspx ). From this site you can select a date and time (down to 15-minut e intervals) and you get a traffic speed map for that time. This could be useful to determine if gridlock wa s worse on some days compared to others.

Texas A&M data http://dallas.tamu.e du/Weather/index.html This web site has data from two sites near Da llas. The sites are run by the Texas A&M Dallas Agricultural Research and Extension Center (phone: 972.231.5362), and details are sketchy. The locations are not specified, although one is on a research farm (Prosper) and the other is just called “Dallas.” The “Dallas” site has, by date, max/min soil temperature, max/min air temperature, max/min RH, a single column labe led “wind” (no units indicated on any of the columns), max/min soil moisture, and total rai n. Some years have a column labeled ET_o (evapotranspiration?). At the end of each month is a row for monthly medians for each column and another row with the max, min, or total for ea ch column (depending on the variable). The Prosper site has the same variables, plus “R AD” (radiation?), wind sp eed, wind direction and battery voltage. The Dallas site has data ar chived from 2000 and the Prosper site has data archived from 1997. Given how important soil mo isture measurements are for modelers, it may be useful to investigate this database further to determine the location of the sites and the robustness of the soil moisture data.

Lower Colorado River Authority network http://hydromet.lcra.org/index2.shtml Lower Colorado River Authority network. This we b page has a wealth of information regarding measurements throughout the Colorado River wate rshed (which extends from NW to SE of Austin, becoming quite narrow at Matagorda Bay). The network is most dense around Austin. They have: rainfall (24-hr accumulation, ac cumulation since midnight, and the most recent measurement); stage, flow, lake level, air temp erature, relative humidity, and conductivity data, shown on maps. You can download hi storic data for a single site (precipitation, air temperature, relative humidity, wind speed and direction), but this is not very practical for obtaining data from many sites. It is stated that real-time data ar e provisional, but there is no indication about the quality of the archived data. It may be worth inve stigating if it is possible to obtain archived data directly from the agency.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 130 Texas A&M agricultural weather site http://texaset.tamu.edu/weatherstns.php This one would be useful for Texas meteorol ogical comparisons of precipitation (not many CAMS sites have precipitation), temp, RH, wind speed, wind directi on, and solar radiation. Pretty good coverage in East Te xas. Hourly data should be simple to download and compare with model results.

Soil Climate Analysis Network, US Agriculture Department http://www.wcc.nrcs.usda.gov/scan/ This site only gives soil parameters - no standard met. There is only one site (Prairie View) in the region of Texas that may be useful to TexAQS 20 06 participants. Nonetheless, it may useful for comparison of soil models and parameterizations in meteorological models, since soil data is so sparse in the east Texas region.

Louisiana agricultural weather data network http://www.agctr.lsu.edu/subjects/weather/ This site is specific to the state of Louisiana. It gives meteorological and soil parameter data at about 20 sites evenly distributed throughout Loui siana. The data are not so convenient to download. But the soil parameters may be useful for meteorological model soil data comparisons.

Louisiana Universities Marine Consortium weather network. http://weather.lumcon.edu/ This web site includes measurements from 5 sites in Louisiana run by LUMCOM (Louisiana Universities Marine Consortium). Four of th e sites are on platforms over water. The 5 th site is somewhat inland, but looks like it’s in a marshy/we tlands type of area. The Lake Pontchartrain station is in the northern part of the lake. Meteorological datainclude: atmospheric pressure, humidity, temperature, winds, so lar radiation, and pr ecipitation. Hydrogra phic instrumentation include: chlorophyll probe, conductivity probe, and a sonde, 6600. Three other sites have the same instruments: Tambour Bay, Southwes t Pass/Miss River and LUMCOM. The Tambour Bay and Southwest sites are off the LA coast, on pl atforms. LUMCOM is the slightly inland site and also has a co-located 915-mHz wind profile r with RASS. The Audubon/Miss River site is on a floating structure near the coast (from the pictur e is looks like it’s in a harbor). It only has atmospheric pressure, humidity, and temperature for meteorological measurements. It has the same hydrographic measurements as Lake Pontch artrain, plus a wet chemical in-situ nitrate analyzer. The web site is comprehensive, with a map a nd much information for each station. There are records regarding calibrations a nd inspections, implying that these sites are well maintained. These appear to be good sites for modelers to obt ain coastal meteorological data for Louisiana. The archived files are easy to access.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 131 CAMS (TCEQ organized surface mete orological and chemical data) http://www.tceq.state.tx.us/nav/eq/mon_sites.html This website includes information regarding the de tails of the TCEQ measurements sites. Many sites have both meteorology and chemistry measuremen ts. Some have just one or the other. All chemistry sites appear to have ozone measur ements, but some also include NO, NO2, and perhaps other important constituents. Those w ith meteorology tend to have temperature and winds, perhaps precipitation. This site has two links: 1) TCEQ’s Air Monitoring Sites (Regional Map) pr ovides details about the TCEQ's air monitoring sites and air polluti on, weather and other parameters measured at each site. 2) Air Monitoring Sites (Table)

Provides a user interface to view sortable list of locations and descrip tions of monitoring sites operated by the TCEQ and other enti ties around the state as well as link to photos of sites, lists of parameters monitored, and current measurements.

This is useful for modelers who want to know th e locations of monitoring stations, and what is monitored at each station. http://www.tceq.state.tx.us/compliance/m onitoring/air/monops/historical_data.html This page provides access to two sources of po llutant and weather data. The first source, the Texas Commission on Environmenta l Quality (TCEQ) and local monitoring networks, provides hourly pollutant and weather data from 1972 to 2004. The second source, the U.S. Environmental Protection Agency (EPA), provide s data summaries and hourly data collected since 1982 on numerous pollutants and meteorologi cal parameters in Texas and other states.

A useful site for modelers to downloa d hourly surface data for model evaluation.

METARs (NWS surface data) http://www.nndc.noaa.gov/cgi-bin/nndc/buyOL-001.cgi The Unedited Surface Weather Observations prod uct consists of unedited hourly observations from over 700 U.S. locations. There is a charge to access this data online, but not if your domain is .gov, .mil, or .edu. (More details on the web site.) The time range of the available data is from July 1, 1996 to two days ago. A useful site for obtaining surface observations for model evaluation.

Oklahoma air quality monitors http://www.deq.state.ok.us/AQDnew/monitoring/index.htm This site has the details of the air quality mon itoring stations in Oklahoma. There are several monitoring sites north of the Te xas-Oklahoma border that would be useful for southerly flow events (e.g., looking at transport from Dallas to Oklahoma). For a graphical display, the site links to the EPA site http://www.airnow.gov/index.cfm?ac tion=airnow.currentconditions , where the user can click on the state of Oklahoma to see the Oklahoma observations. Text data includes real-time data (for today and yesterday). The user can sort by pollutant or by station. Archived data includes 8-hour averages of ozone and CO, organized by year. Within each year is the date and amount of the 4 highest readi ngs for each station. One-hour ozone exceedances are also available in this format. It does not appear that data ot her than the 4 highest readings per year are available via the web. This site is probably somewhat useful for modelers.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 132 Upper Air Data

ESRL (formerly ETL) Profiler Network, South Central Texas http://www.etl.noaa.gov/et7/data/ The ESRL (formerly ETL) network page allows acce ss to real-time and arch ived plots of profiler winds and other profiler data. Real-time plots are provided thr ough a clickable map interface. Archived plots and ASCII data can be downloaded for single profilers. A trajectory tool allows the calculation of forward and backward trajectori es using profiler data. The site includes all regular wind profilers from the NOAA and TCEQ network as well as all those installed for the TexAQS-II field program. The data include pr ofiler winds and signal-to-noise ratio, RASS virtual temperature and virtual potential temper ature, and surface meteorological observations from profiler sites. Data should remain available for several months after the experiment, as well as the profiler trajectory tool.

NOAA National Profiler Network graphical display http://www.profiler.noaa.gov/npn/ The NOAA site used to include all permanent pr ofilers, but now it appears to contain only the profilers in the NOAA demonstration network, in cluding Ledbetter, Pales tine, and Jayton in Texas. Users can request real-time plots or ge nerate plots using arch ived data. There is considerable flexibility in the onl ine data plotting interface. Arch ived data are available from the web site hosts.

Rapid Update Cycle (RUC) soundings http://rucsoundings.noaa.gov/ This sounding page allows the user to generate plots or ASCII data dumps of soundings from rawinsondes, profilers, and RUC/MAPS forecasts. The output is Java-based, allowing mouseover data information and animation/looping of soundings. The interface requires the user to know the name or site ID’s of the stations to pl ot. Most of the data are available only in realtime or near-real-time, except that an online rawinsonde arch ive was begun early in 2006. Perhaps the most useful aspect of the web site is the ability to plot forecast soundings from the RUC model. These forecasts ar e available for any arbitrary location and extend up to 12 hours into the future, so they provide detailed guidan ce for mixing heights, vertical wind shear, and convection.

University of Wyoming sounding page http://weather.uwyo.e du/upperair/sounding.html This web site allows the user to select a sta tion using a clickable map and generate graphical soundings or ASCII data output from real-time or archived rawinsonde observations. The output format includes all common sounding diagram types and ASCII data formats. Large amounts of data would be difficult to obtain, but this site is the best available on the web for individual archived soundings.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 133 ACARS aircraft observations http://amdar.noaa.gov/ ACARS observations are in situ meteorological observations made by commercial aircraft. The data include temperature, wind, and often dew point. The wind precision is not very good, but the temperature and dew point data are useful for estimating mixing hei ghts and their diurnal variation. Most ACARS observati ons in Texas come from the Dallas-Fort Worth area, usually about two dozen per day. Much less frequent obs ervations are available from Houston and other major airports. The data are not freely availabl e in real time on the web, but they are available for research purposes upon approval by NOAA. Texas A&M presently receives ACARS data but is not funded by TCEQ to process or use the data for analysis or forecasting during 2006.

Coastal and Buoy Data

Texas Coastal Ocean Observation Network, Te xas A&M Corpus Christi, Conrad Blucher Institute http://lighthouse.tamucc.edu/TCOON/HomePage Large network of coastal stations. Some of the reported stations ar e regular NOAA or other agency stations, and these are not identified as such. The additional stations seem to primarily provide water level, water temperature, and air te mperature. Machine-readable historical data are available. Some QA is a pparently done, but specifications are not easily found on the web site. Possibly useful for improving resolution of model validations for simple parameters.

NDBC (National buoy data) http://www.ndbc.noaa.gov/Maps/WestGulf.shtml Provides listings of hourly meteor ological data (air and sea-surf ace temperature, winds, pressure, etc.) and wave data for each meteorological buoy in the Gulf of Mexico (and elsewhere around the U.S.). Meteorological data ar e archived back as far as 1990 fo r some sites. Also a section gives data on ocean currents as a function of depth. Buoy and other instrument locations are displayed on a map, and data are obtained by cl icking on the site of in terest. Recent ship observations are also listed at this site, and the tri-annual Mariners Weather Log.

Houston/Galveston Port Meteorological Office http://www.srh.noaa.gov/hgx/marine/pro.htm Houston/Galveston Port Meteorological Office Site includes a description of needs for maritime mete orological data and the role of this office in facilitation of the Voluntary Observing Ship (VOS ) Program. The office also “works within the framework of the Shipboard Environmental (Data) Acquisition System (SEAS), by which meteorological data are collected and transmitted to NCEP, for inclusion in the major data bases. Under Past Weather , this site has climatol ogical data and daily information for several Texas land stations around the Gulf of Mexico, incl uding daily high and low temperatures, wind, precipitation, and some other meteorological data . The monthly Texas Climatic Bulletins and other climatological products and in formation are available at this si te. We were unable to locate any actual shipboard data from this site (how ever, some current data could be found on the NDBC site).Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 134 Satellite Data

Space Science and Engineering Center, University of Wisconsin, Madison http://www.ssec.wisc.edu/ Comprehensive archive of data, products, and downloadable processing software for geosynchronous and polar-orbiti ng satellites, including GOES-11 and -12 and MODIS data from Terra and Aqua. A host of real-time satellite images and products are also available, some stored for 7 days. Routine meteorological data are also available for McIdas users.

NASA Aura TES step and stare observations http://tes.jpl.nasa.gov TES is an infrared, high resolution, Fourier Tran sform spectrometer coveri ng the spectral range 650 - 3050 cm-1 (3.3 - 15.4 μm) at a spectral resoluti on of 0.1 cm-1 (nadir viewing) or 0.025 cm1 (limb viewing). Launched into a polar sun-sync hronous orbit (13:38 hrs local mean solar time ascending node) on July 15, 2004, the TES orbit repeats its ground track every 16 days (233 orbits), allowing global mapping of the vertical distribution of tropospheric ozone and carbon monoxide along with atmospheric temperature, wa ter vapor, surface properties (nadir), and effective cloud properties (nadir). TES has a fixe d array of 16 detectors, which in the nadir mode, have an individual footprint of approxima tely 5.3 x .5 km. In order to increase the signalto-noise ratio, these detectors are averaged together to produ ce a combined footprint of 5.3x8.4 km. TES has two basic observational modes: th e global survey mode, where observations are taken 1.3 degrees apart in latit ude, and the "step-and-stare" m ode, where the separation between observations is approximately 35 km along the or bit. This step-and-stare mode was used extensively throughout the TexAQS 2006 campaign. Maps of these profiles can be found at http://tes.jpl.nasa.gov/Tex AQS_2006/main_SS_TEXAQS_2006.html

Contact information: kevin.bowman@jpl.nasa.gov

NASA CALIPSO observations http://www-calipso.larc.nasa.gov/ The Cloud-aerosol lidar and Infr ared Pathfinder satellite Obse rvations (CALIPSO) satellite mission was launched on April 28, 2006 for a pl anned 3-year mission. CA LIPSO is flying in formation with Aqua, Aura, CloudSat, and Pa rasol satellites as pa rt of the Afternoon Constellation or A-train. The CALIPSO payloa d consists of three instruments: the CloudAerosol LIdar with Orthogonal Po larizaton (CALIOP); the Infrared Imaging Radiometer (IIR) and the Wide Field Camera (WFC). CALIPO is a nadir-pointing instrument which provides profile measurements of aerosol backscatter at 532 and 1064 nm and depolarization at 532 nm. The IIR provides calib rated radiances at 8.65 μ m, 10.6 μ m, and 12.05 μ m over a 64 km swath centered on the lidar footprint a nd a pixel resolution of 1 km. The WFC consists of a single channel covering the 620 nm to 670 nm spectral region providing im ages of a 61 km swath with a spatial resolution of 125 m in a band 5 km a bout the nadir track and 1000 m elsewhere. For TexAQS 2006 campaign, CALIPSO quick turn-around browse images were produced to identify aerosol and cloud layers for flight planning activities. Observations were also synthesized into aerosol modeling systems to better understand the origin of surface and elevated aerosol features from regions outside th e experiment domain.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 135 Examples of aerosol browse images used during TexAQS can be found at http://www-calipso.larc.nasa.gov/products/lidar/

Contact information: Charles.R.Trepte@nasa.gov

NOAA and DoD satellite images http://www.class.noaa.gov/ nsaa/products/welcome;jse ssionid=1C0E54F015C2813E5A9ACFC2 2C675F90 The National Oceanic and Atmospheric Admini stration (NOAA) Comprehensive Large Arraydata Stewardship System (CLASS ) is NOAA's premier on-line faci lity for the distribution of NOAA and US Department of Defense (DoD) Polar-orbiting Operational Environmental Satellite (POES) data and derived data produ cts. CLASS is operated by the Information Processing Division (IPD) of the Office of Satell ite Data Processing and Distribution (OSDPD), a branch of the National Environmental Satell ite, Data and Information Service (NESDIS). CLASS maintains an active partnership with NOAA 's National Climatic Data Center (NCDC). NCDC, the permanent US Archive for POES data and derived data products, supports CLASS through a user-interactive Help Desk facility and through the provisi on of POES supporting documentation, including the NOAA Polar Orbite r Data (POD) User's Guide and the NOAA KLM User's Guide. Additionally, NCDC and CLA SS share data distribution responsibilities for Defense Meteorological Satellite Program (DMS P) data under a Memorandum of Understanding with the National Aeronautics and Space Administration (NASA) for the Earth Observing System (EOS) Program. CLASS provides data free of charge. Anyone can search the CLASS catalog and view search results through CLASS's World Wide Web (WWW ) site. Users who wish to order data are required to register with their names and email a ddresses. CLASS distributes data to those users via FTP services. CLASS (originally called Satellite Active Arch ive), was established as a demonstration prototype for electronic distri bution of POES data in 1994, and became operational in July 1995. During that first month, 379 Advanced Very High Resolution Radiometer (AVHRR) Level 1b data sets were distributed to 27 customers via th e emerging Internet. During the first five years of operation, the average monthly volume of data di stribution increased to 65,000 data sets with a total size of 1.2 TB, and the SAA customer base grew to more than 10,000 registered customers. The active archive was expanded during that pe riod to include TIROS Operational Vertical Sounder (TOVS) data, Defense Meteorological Sa tellite Program (DMSP) data, Radarsat Synthetic Aperture Radar (SAR) imagery, operati onal (near-term) satellite-derived products, and climatic (time-series) sa tellite-derived products.

NASA Earth Observatory natural hazards http://earthobservatory.nasa.gov/NaturalHazards/ This is a NASA site that has awesome satellite images due to the followi ng natural phenomena: crops & drought; dust & smoke, fires, floods, seve re storms, and volcanoes. The images are organized by event, and are free to all. They ju st ask for proper acknowle dgment. This site is probably of limited value to modelers, but for certain events, such as the Saharan dust events that occurred during TexAQS II, the images may add some visual interest for a case study presentation.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 136 NASA MODIS Rapid Respon se System images http://rapidfire.sci.gsfc.nasa.gov/realtime/2006297/ This site has images from MODIS (Terra and Aqua). Images are archived by day, and can be downloaded. This site may be somewhat useful for modelers.

NASA Aqua AIRS retrieved CO profiles http://asl.umbc.edu/pub/mcmillan/www/index.html#calendar AIRS output for the TexAQS II field campaign. Th ere is a clickable cale ndar for a view of the data. Please work with Dr. Wall ace McMillan if interested in usi ng the data (contact information is on the web site).

Solar Radiation Data

Texas Solar Radiation data, from a solar energy research group at UT http://www.me.utexas.edu/~solarlab/tsrdb/ This site has solar radiation data for 15 sites th roughout Texas. The data intervals and times of coverage vary by station, ranging from 15 minute data to monthly averages. Data stops in 2003 or earlier for many of the stations. Data reporte d: Global horizontal, dir ect normal, and diffuse horizontal (W m -2 ). Monthly averages include temperature (degrees C). Data are easy to access. This site may be moderately useful for modelers.

National Renewable Energy Lab (solar radiation data) http://rredc.nrel.gov/s olar/new_data/confrrm/ Cooperative Networks for Renewable Resource M easurements (CONFRRM). This network was designed to capture long-term so lar radiation and wind measurements. There are 5 sites in Texas, however the last month s howing data for all sites is Marc h 2000. Therefore, data on this site are not useful for mode lers working on summers 2000 – 2006.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 137 Large, Multi-field Data Sets

MADIS http://madis.noaa.gov/ The Meteorological Assimilation Data Ingest System (MADIS) is dedicated toward making value-added data available from the Nationa l Oceanic and Atmospheric Administration's (NOAA) Earth System Research Laboratory (ESRL) Global Syst ems Division (GSD) (formerly the Forecast Systems Laboratory (FSL)) for th e purpose of improving weather forecasting, by providing support for data assimilation, nu merical weather prediction, and other hydrometeorological applications. MADIS subscribers have access to an integrated, reliable and easy-to-use database containing the real-time and archived observational datasets described below. Also available are real-time gridded surface analyses that assimilate all of the MADIS surface datasets (including the very dense integrated mesonet data). The grids ar e produced by the Rapid Update Cycle (RUC) Surface Assimilation System (RSAS) that runs at ESRL/GSD, whic h incorporates a 15-km grid stretching from Alaska in the north to Central Am erica in the south, and al so covers significant oceanic areas. RSAS grids are valid at the top of each hour, and are updated every 15 minutes.

• Observations

o Meteorological Surface

 METAR

 SAO

 Maritime

 Modernized NWS Cooperative Observer

 Integrated Mesonet

 Observations from local, state, and fe deral agencies and private mesonets (including GPSMET water vapor)

o Radiosonde

o NOAA Profiler Network

o Hydrological Surface

o Automated Aircraft

 Automated Aircraft Reports

 Profiles at Airports

o Multi-Agency Profiler

o Radiometer

o Satellite Wind

 GOES Operational 3-Hour

 GOES Experimental 1-Hour

o Satellite Sounding

 NOAA POES

o Satellite Radiance

 NOAA POES

o Snow

• Grids

o RSAS Surface Analyses

TCEQ Air Pollution Events http://www.tceq.state.tx.us/complian ce/monitoring/air/monops/sigevents06.html The TCEQ Air Pollution Events web pages prov ide preliminary analyses of large-scale high ozone and/or particulate events in Texas. The analyses in clude satellite imagery, webcam imagery, ozone contour animations, ozone plume an imations, backward air trajectories, upper airFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 138 data graphs, and pollution data time series gra phs. The discussions describe the intensity and geographic coverage of each event. The discussions also report any trans port related aspects to the pollution, if appropriate, and provide an es timate of background levels and local add-on for

ozone cases.

EDAS (NCEP grid reanalysis) http://www.cdc.noaa.gov/cdc/reanalysis/reanalysis.shtml It is a website for the NCEP/NCAR Reanalysis Project at the NOAA/ESRL Physical Sciences Division This page points you to information on th e NCEP/NCAR Reanalysis project and the implementation of a netCDF-based, internet-acc essible, data service at NOAA/ESRL PSD for this set of data products. * The 6-hourly and daily da ta currently available on-line. * The monthly and other derive d data currently available on-line.

This site also has links to other reanalysis project sites (e.g., ECMWF).

NCEP/NCAR Reanalysis http://dss.ucar.edu/pub/reanalysis/ This site includes the following NCEP/NCAR REANALYSIS databases. * DSS Reanalysis archives. Project Overview * Project Description - The project motivation and obj ectives, cooperative arrange ment between NCEP and NCAR, and other published documentation are outlined * Model Description * Project Status
* Other Related Sites Data Product Description More than 20 different data products are outpu t from the Reanalysis data assimilation, model run, and model forecast. These products are de fined in terms of the NCAR archive names, physical variables, resolutions (temporal and spat ial), and media storage size. CDROMS are also used to distribute select ed reanalysis products. This web site includes much detail on all of the data bases used, etc. * 2006AUG10 --All 1948-2006JUL pgb.f00 and gr b2d files are now available on line for registered users.
* 2006AUG10 --JUL 2006 data files are rele ased. All 1948-2006JUL reanalysis files are available. * 2006APR20 --Public (non-restricted) vers ion of 200309-200602 prepqm files are released.
* 2006APR11 --2005 annual cdrom is releas ed. All 1950-2005 reanalysis annual cdroms are available.
* 2006MAR28 --2005OCT-2006FEB rean alysis forecasts are released.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 139 * 2005Apr20 --2004OCT-2004DEC reruns to fix sea-ice problems are released. * 2005Apr19 --2004AUG and 2004SEP reruns to fix sea-ice problems are released. * 2005Apr08 --There w ill be a rerun from 2004080100 to 2005032212 due to sea-ice data problem. The 200501 and 200502 results are in. * 2003Aug04 --NCEP-NCAR Reanalysis Temperature Change Plots, 1948-2002 * DSS Reanalysis archives. Project Overview * Project Description - The project motivation and obj ectives, cooperative arrange ment between NCEP and NCAR, and other published documentation are outlined * Model Description * Project Status
* Other Related Sites Data Product Description More than 20 different data products are outpu t from the Reanalysis data assimilation, model run, and model forecast. These products are de fined in terms of the NCAR archive names, physical variables, resolutions (temporal and spat ial), and media storage size. CDROMS are also used to distribute select ed reanalysis products. This web site includes much detail on all of the data bases used, etc.Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 140 APPENDIX 3. INSTITUTIONAL AFFILI ATIONS AND E-MAIL ADDRESSES FOR MEMBERS OF THE RAPID SCIENCE SYNTHESIS WORKING GROUPS AND OTHER COOPERATORS

Dave Allen, University of Texas at Austin allen@che.utexas.edu Wayne Angevine, NOAA Earth System Resear ch Laboratory wayne.m.angevine@noaa.gov Elliot Atlas, University of Miami e.atlas@miami.edu Bob Banta, NOAA Earth System Resear ch Laboratory r obert.banta@noaa.gov Tim Bates, NOAA Pacific Marine Envi ronmental Laboratory tim.bates@noaa.gov Carl Berkowitz, DOE Pacific Northw est Laboratory carl.berkowitz@pnl.gov Veronique Bouchet, Meteor ological Service of Canada veronique.bouchet@aec.gc.ca Kevin Bowman, NASA Jet Propulsion Labor atory, Caltech kevin.bowman@jpl.nasa.gov Pete Breitenbach, Texas Commission on Enviro nmental Quality pbreiten@tceq.state.tx.us Chuck Brock, NOAA Earth System Research Laboratory charles.a.brock@noaa.gov Steve Brown, NOAA Earth System Resear ch Laboratory steven.s.brown@noaa.gov Daewon Byun, University of Houston daewon.byun@mail.uh.edu Greg Carmichael, University of Iowa gregory-carmichael@uiowa.edu Bill Carter, University of California, Riverside carter@mail.cert.ucr.edu Ellis Cowling, North Carolina State University ellis_cowling@ncsu.edu Lisa Darby, NOAA Earth System Res earch Laboratory lisa.darby@noaa.gov Joost de Gouw, NOAA Earth System Re search Laboratory joost.degouw@noaa.gov Basil Dimitriades, North Carolina State University basildi@hotmail.com Bright Dornblaser, Texas Commission on Envi ronmental Quality bdornbla@tceq.state.tx.us Mark Estes, Texas Commission on Envir onmental Quality mestes@tceq.state.tx.us Alan Fried, National Center for At mospheric Research fried@ucar.edu Maedeh Faraji, University of Texas at Austin mfaraji@mail.utexas.edu Cari Furiness, North Carolina State University cari_furiness@ncsu.edu Noor Gillani, University of Alabama in Huntsville gillani@nsstc.uah.edu Wanmin Gong, Meteorological Service of Canada wanmin.gong@ec.gc.ca Georg Grell, NOAA Forecast System s Laboratory georg.a.grell@noaa.gov Mike Hardesty, NOAA Earth System Resear ch Laboratory mike.hardesty@noaa.gov Scott Herndon, Aerodyne Inc. herndon@aerodyne.com John Holloway, NOAA Earth System Res earch Laboratory john.s.holloway@noaa.gov Harvey Jeffries, University of NC at Chapel Hill harvey@unc.edu John Jolly, Texas Commission on Environm ental Quality jjolly@tceq.state.tx.us Dick Karp, Texas Commission on Envir onmental Quality dkarp@tceq.state.tx.us Siwan Kim, NOAA Earth System Res earch Laboratory siwan.kim@noaa.gov Susan Kemball-Cook, Environ Corp skemballcook@environcorp.com Bryan Lambeth, Texas Commission on Environmental Quality blambeth@tceq.state.tx.us Barry Lefer, University of Houston blefer@uh.edu Deborah Luecken, US Environmental Pr otection Agency luecken.debora@eps.gov John McHenry, Baron Advanced Meteorologi cal Systems john.mchenry@baronams.com Stuart McKeen, NOAA Earth Sy stem Research Laboratory stuart.a.mckeen@noaa.gov Wallace McMillan, University of Mary land, Baltimore County mcmillan@umbc.edu Jeffery McQueen, NOAA National Weat her Service jeff.mcqueen@noaa.gov Johan Mellqvist, University of Götebor g, Sweden johan.mellqvist@rss.chalmers.seFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 141 Ann Middlebrook, NOAA Earth System Resear ch Laboratory ann.m.middlebrook@noaa.gov Gary Morris, Valparaiso Univ ersity gary.morris@valpo.edu John Nielsen-Gammon, Texas A&M University n-g@tamu.edu Jonathan Andy Neuman, NOAA Earth System Research Laboratory andy.neuman@noaa.gov John Nowack, NOAA Earth System Re search Laboratory john.nowak@noaa.gov Hans Osthoff, NOAA Earth System Re search Laboratory hans.osthoff@noaa.gov David Parrish, NOAA Earth System Res earch Laboratory david.d.parrish@noaa.gov Ryan Perna, University of Houston ryan.perna@mail.uh.edu Brad Pierce, NOAA Satellite and Inform ation Service robert.b.pierce@nasa.gov Particia Quinn, NOAA Pacific Marine Enviro nmental Laboratory particia.k.quinn@noaa.gov Bernhard Rappenglück, University of Houston brappenglueck@uh.edu James Roberts, NOAA Earth System Resear ch Laboratory james.m.roberts@noaa.gov Ted Russell, Georgia Institute of Technology trussell@ce.gatech.edu Tom Ryerson, NOAA Earth System Resear ch Laboratory thomas.b.ryerson@noaa.gov Ken Schere, US Environment Protecti on Agency schere.kenneth@epa.gov Christoph Senff, NOAA Earth System Res earch Laboratory christoph.senff@noaa.gov David Sullivan, University of Texas at Austin sullivan231@mail.utexas.edu Michael Trainer, NOAA Earth System Res earch Laboratory michael.k.trainer@noaa.gov Youhua Tang, University of Iowa youhua.tang@noaa .gov Sara Tucker, NOAA Earth System Res earch Laboratory sara.tucker@noaa.gov William Vizuete, University of North Caro lina at Chapel Hill vizuete@email.unc.edu Carsten Warneke, NOAA Earth System Laboratory carsten.warneke@noaa.gov Allen White, NOAA Earth System Resear ch Laboratory allen.b.white@noaa.gov Jim Wilczak, NOAA Earth System Research Laboratory james.m.wilczak@noaa.gov Eric Williams, NOAA Earth System Resear ch Laboratory eric.j.williams@noaa.gov David Winker, NASA Langley Research Center d.m.winker@larc.nasa.gov Yulong Xie, DOE Pacific Nort hwest Laboratory yulong.xie@pnl.gov Greg Yarwood, Environ Corp gyarwood@environcorp.comFinal Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 142 APPENDIX 4. GUIDELINES FOR FORMULATION OF SCIENTIFIC FINDINGS TO BE USED FOR POLICY PURPOSES The following guidelines in the form of check list questions were developed by the NAPAP Oversight Review Board to assist scientists in fo rmulating presentations of research results to be used in policy decision processes. 1) IS THE STATEMENT SOUND? Have the central issues been clearly identified? Does each statement contain the distil led essence of present scientific and technical understanding of the phenomenon or process to which it applies ? Is the statement consistent with all relevant evidence – evidence developed either through NAPAP [or TexAQS] research or through analysis of research conducted outside of NAPAP [or TexAQS II]? Is the statement contradicted by any important evidence develo ped through research inside or outside of NAPAP [or TexAQS II] Have apparent contradictions or interpretations of available evidence been considered in formulati ng the statement of principal findings? 2) IS THE STATEMENT DIRECTIONAL AND, WHERE APPROPRIATE, QUANTITATIVE? Does the statement correctly quant ify both the direction and magnitude of trends and relationships in the phenomenon or process to whic h the statement is relevant? When possible, is a range of uncerta inty given for each quantitative result ? Have various sources of uncertainty been id entified and quantified, for exampl e, does the statement include or acknowledge errors in actual measurements, standard errors of estimate, possible biases in the availability of data, extr apolation of results beyond the ma thematical, geographical, or temporal relevancy of available information, etc. In short, are there numbers in the statement? Are the numbers correct? Are the numbers rele vant to the general meaning of the statement ? 3) IS THE DEGREE OF CERTAINTY OR UNCERTAINTY OF THE STATEMENT INDICATED CLEARLY? Have appropriate statistical tests been applied to the data used in drawing the conclusion set forth in the statement? If the statement is based on a mathematical or novel conceptual model, has the model or co ncept been validated? Does the statement describe the model or concept on which it is base d and the degree of validity of that model or concept? 4) IS THE STATEMENT CORRECT WITHOUT QUALIFICATION? Are there limitations of time, space, or other special ci rcumstances in which the statement is true ? If the statement is true only in some circumstances , are these limitations described adequately and briefly? 5) IS THE STATEMENT C LEAR AND UNAMBIGUOUS? Are the words and phrases used in the statement understandable by th e decision makers of our society ? Is the statement free of specialized jargon? Will too ma ny people misunderstand its meaning? 6) IS THE STATEMENT AS CONCISE AS IT CAN BE MADE WITHOUT RISK OF MISUNDERSTANDING? Are there any excess words, phrases, or ideas in the statement which are not necessary to communicate the mean ing of the statement? Are there so many caveats in the statement that the statement itself is trivial, confusing, or ambiguous? 7) IS THE STATEMENT FREE OF SCIE NTIFIC OR OTHER BIASES OR IMPLICATIONS OF SOCI ETAL VALUE JUDGMENTS? Is the statement free of influence by specific schools of scientific thought? Is the st atement also free of words, phrases, or concepts that have political, ec onomic, ideological, religious, moral, or other personal-, agency-, or organization-specific va lues, overtones, or imp lications? Does the choice of how the statement is expressed rather than its specific words suggest underlying biases or value judgments ? Is the tone impartial and free of special pleading? If societal value judgments have been discussed, have these judgments been id entified as such and described both clearly and objectively?Final Rapid Science Synthesis Report: Findings from TexAQS II

31 August 2007 143 8) HAVE SOCIETAL IMPLICATIONS BEEN DESCRIBED OBJECTIVELY? Consideration of alternative courses of acti on and their consequences inherently involves judgments of their feasibility and the importance of effects. For this reason, it is important to ask if a reasonable range of alte rnative policies or courses of action have been evaluated? Have societal implications of alternative course s of action been stated in the following general form?:
"If this [particular option] were adopted then that [particular outcome] would be expected." 9) HAVE THE PROFESSIONAL BIASES OF AUTHORS AND REVIEWERS BEEN DESCRIBED OPENLY? Acknowledgment of potential sources of bias is important so that readers can judge for themselves the cr edibility of reports and assessments.