UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

WASHINGTON D.C. 20460

OFFICE OF THE ADMINISTRATOR

SCIENCE ADVISORY BOARD

May 7, 2010

EPA-SAB-10-007

The Honorable Lisa P. Jackson

Administrator U.S. Environmental Protection Agency 1200 Pennsylvania Avenue, N.W. Washington, D.C. 20460

Subject: Review of EPA’s draft entitled, “Risk and T echnology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory Board

with Case Studies – MACT I Petroleu m Refining Sources and Portland Cement Manufacturing”

Dear Administrator Jackson:

In response to a request from EPA’s Offi ce of Air Quality Planning and Standards (OAQPS), the Science Advisory Board (SAB) conve ned an expert panel to review their draft document entitled, “Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory Board w ith Case Studies – MACT I Petroleum Refining Sources and Portland Cement Manufacturing” (J une 2009). This draft document, hereafter referred to as the Agency’s draft RTR docum ent, describes EPA’s proposed methodology for assessing residual risk from indus trial emissions of hazardous air pollutants. The proposed methodologies are demonstrated through the use of two case studies: (1) petroleum refineries, and (2) Portland cement manufacturing facilitie s. The SAB was asked to comment on seven topics, including the derivation of emissions estimates, inputs for the dispersion modeling, selection of dose-response valu es, estimating chronic inhalation exposures, developing estimates of acute inhalation risk, developing an ecolo gical risk assessment, and overall risk

characterization.

The Panel commends the Agency on its efforts to develop a technically sound and practical approach for the challenging task of residual risk assessment. The case studies presented in the Agency’s draft RTR document pr ovide valuable insight into the strengths and limitations of the data inputs and methodology. While EPA proceeds with its RTR assessments,

the SAB Panel recommends a number of modifications to improve the scientif ic basis of the data inputs and methodology. A more de tailed description of the technical recommendations is contained in the body of the repo rt, with the key points and re commendations highlighted here.• The SAB found emissions estimates to be one of the most critical input s to a residual risk assessment and an important area needing improvement. As a starting point for its assessments, EPA has proposed to use “actual” emissions reported to the National Emissions Inventory (NEI), which would then be refine d through internal EPA review and the public notice and comment process. However, EPA’s case studies and outside evaluations suggest that the resulting emissions estimates may be bias ed toward underestimation. To address this concern, the Panel recommends that EPA modi fy its methodology to first assess residual risks associated with facility-specific “allo wable” emissions, reflecting current regulatory limits. As a second step, the Agency could m odel estimates of actual facility emissions to

assess current risks.

• In the particular case of radionuclides emissi ons from Portland cement facilities, the Panel found that information presen ted in the Agency’s draft document requires further

characterization before inclus ion in the RTR assessment. Isotope-specific emissions information needs to be developed to support ri sk assessments for Portland cement plants and

other facilities that emit radionuclides.

• The Panel found EPA’s approach to selecting dos e-response chronic toxi city values to be generally sound, but recommends the Agency more closely scrutinize values that emerge as

drivers of risk assessment results. The Panel supports the use of the Integrated Risk Assessment System (IRIS) as the preferred da tabase for chronic dose-response data. The Panel also strongly recommends that EPA deve lop toxicity values for all Hazardous Air Pollutants (HAPs) insofar as the data permit, and that it update IRIS in a timelier manner. The Panel recognizes that there are more gaps and inconsistencies in acute health data than in chronic data, and cautions that acute values used for residual risk assessments must be examined carefully and may need to be ad justed to ensure they protect sensitive subpopulations.

• The Panel found the ecological ri sk assessment case study presen ted in the Agency’s draft RTR document to be an impressive effort to a ddress an extremely complex issue. To further validate the RTR methodology, the Panel recommends that EPA conduct site-specific evaluations using more established ecological risk assessment methods and compare the outcomes with TRIM.FaTE predictions.

• Finally, the Panel found that EP A’s RTR process itself presents an incomplete picture of risks from facilities such as petroleum refine ries, which fall into more than one regulatory source category. The Agency should ensure its risk characterizations clearly explain this limitation. Furthermore, the Panel agrees that RTR assessments will be most useful to decision makers and communities if results are presented in the broader context of aggregate and cumulative risks, including background conc entrations and contributions from other

sources in the area.

iiiii The SAB appreciates the opportunity to provide EPA with advi ce on this important subject. We look forward to re ceiving the Agency’s response.

Sincerely,

/Signed/ /Signed/

Dr. Deborah L. Swackhamer, Chair Dr. Jana Milford, Chair EPA Science Advisory Board SAB RTR Methods Review Paneliv 

### NOTICE

This report has been written as part of the activities of th e EPA Science Advisory Board, a public advisory group providing extramural scientific information and advice to the Administrator and other officials of the Environmental Protection Agency. The Board is structured to provide balanced, expert assessment of scientific matters related to the problems facing the Agency. This report has not been revi ewed for approval by the Agency and, hence, the contents of this report do not necessarily repres ent the views and policies of the Environmental Protection Agency, nor of other agencies in the Executive Branch of the Federal government, nor

does mention of trade names or commercial products constitute a recommendation for use. Reports of the EPA Science Advisory Board are posted on the EPA website at http://www.epa.gov/sab

.v 

### U.S. Environmental Protection Agency Science Advisory Board Risk and Technology Review Methods Panel

CHAIR

Dr. Jana Milford , Professor, Department of Mechanical Engineering, University of Colorado, Boulder, CO

MEMBERS

Dr. G. Allen Burton , Professor and Director, Cooperative Institute for Limnology and Ecosystems Research, School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI

Dr. David Eastmond , Professor and Chair, Department of Cell Biology and Neuroscience, Toxicology Graduate Program, University of California - Riverside, Riverside, CA

Mr. Thomas Gentile , Chief, Air Toxics Section, Bureau of Air Quality Analysis and Research, Division of Air Resources, New York State Depa rtment of Environmenta l Conservation, Albany,

NY

Dr. Gary Ginsberg , Toxicologist, Environmental & Occupational Health, Connecticut Department of Public Health, Hartford, CT

Dr. Judith Graham , Independent Consultant, Independent Consultant, Pittsboro, NC

Dr. Cynthia Harris , Director and Professor, Institute of Public Health, Florida A&M University, Tallahassee, FL

Dr. Thomas W. La Point , Director, Department of Biological Sciences, Institute of Applied Sciences, University of North Texas, Denton, TX

Dr. Abby Li , Senior Science Fellow, Exponent In corporated, San Francisco, CA,

Dr. Randy Maddalena , Scientist, Environmental En ergy Technologies Division, Indoor Environment Department, Lawrence Berkel ey National Laboratory, Berkeley, CA

Dr. John O'Donoghue , Adjunct Associate Professor, Depa rtment of Environmental Medicine, School of Medicine and Dentistry (Box EHSC), University of Roch ester Medical Center, Rochester, NY

Dr Loren Raun , Senior Environmentalist Analyst/Faculty Lecturer, Mayor's Office, City of Houston/Rice University, Houston, TXvi Dr. Mark Rood , Professor, Department of Civil and E nvironmental Engineering, University of Illinois, Urbana, IL

Dr. John Veranth , Research Associate Professor, Department of Pharmacology and Toxicology, University of Ut ah, Salt Lake City, UT

Dr. Chris Walcek , Senior Research Scientist, Atmosphe ric Sciences Research Center, State University of New York, Albany, NY

SCIENCE ADVISORY BOARD STAFF

Dr. Suhair Shallal , Designated Federal Officer, 1200 Pennsylvania Avenue, NW 1400F, Washington, DCvii 

### U.S. Environmental Protection Agency Science Advisory Board

CHAIR

Dr. Deborah L. Swackhamer , Professor and Charles M. Denny, Jr., Chair in Science, Technology and Public Policy and Co-Director of the Water Resources Center, Hubert H. Humphrey Institute of Public Affairs, University of Minnesota, St. Paul, MN

SAB MEMBERS

Dr. David T. Allen , Professor, Department of Chemical Engineering, University of Texas, Austin, TX

Dr. Claudia Benitez-Nelson , Associate Professor, Department of Earth and Ocean Sciences and Marine Science Program, University of South Carolina, Columbia, SC

Dr. Timothy Buckley , Associate Professor and Chair, Division of Environmental Health Sciences, College of Public Health, Th e Ohio State University, Columbus, OH

Dr. Thomas Burke , Professor, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD

Dr. Deborah Cory-Slechta , Professor, Department of Environmental Medicine, School of Medicine and Dentistry, University of Rochester, Rochester, NY

Dr. Terry Daniel , Professor of Psychology and Natural Resources, Department of Psychology, School of Natural Resources, University of Arizona, Tucson, AZ

Dr. George Daston , Victor Mills Society Research Fe llow, Product Safety and Regulatory Affairs, Procter & Gamble, Cincinnati, OH

Dr. Costel Denson , Managing Member, Costech Technologies, LLC, Newark, DE

Dr. Otto C. Doering III , Professor, Department of Agricu ltural Economics, Purdue University, W. Lafayette, IN

Dr. David A. Dzombak , Walter J. Blenko Sr. Professor , Department of Civil and Environmental Engineering, College of Engin eering, Carnegie Mellon University, Pittsburgh,

PA

Dr. T. Taylor Eighmy , Vice President for Research, Office of the Vice President for Research, Texas Tech University, Lubbock, TX

Dr. Elaine Faustman , Professor, Department of Envi ronmental and Occupational Health Sciences, School of Public Heal th and Community Medicine, Univ ersity of Washington, Seattle,

WAviii Dr. John P. Giesy , Professor and Canada Research Chair, Veterinary Biomedical Sciences and Toxicology Centre, University of Saskat chewan, Saskatoon, Saskatchewan, Canada

Dr. Jeffrey Griffiths , Associate Professor, Department of Public Health and Community Medicine, School of Medicine, Tufts University, Boston, MA

Dr. James K. Hammitt , Professor, Center for Risk Anal ysis, Harvard University, Boston, MA

Dr. Rogene Henderson , Senior Scientist Emeritus, Lovelace Respiratory Research Institute, Albuquerque, NM

Dr. Bernd Kahn , Professor Emeritus and Associate Dir ector, Environmental Radiation Center, School of Mechanical Engineering, Geor gia Institute of Technology, Atlanta, GA

Dr. Agnes Kane , Professor and Chair, Department of Pathology and La boratory Medicine, Brown University, Providence, RI

Dr. Nancy K. Kim , Senior Executive, New York Stat e Department of Health, Troy, NY

Dr. Catherine Kling , Professor, Department of Economics, Iowa State University, Ames, IA

Dr. Kai Lee , Program Officer, Conservation and Scie nce Program, David & Lucile Packard Foundation, Los Altos, CA

Dr. Cecil Lue-Hing , President, Cecil Lue-Hing & Assoc. Inc., Burr Ridge, IL

Dr. Floyd Malveaux , Executive Director, Merck Childhood Asthma Network, Inc., Washington,

DC

Dr. Lee D. McMullen , Water Resources Practice Leader, Snyder & Associates, Inc., Ankeny,

IA

Dr. Judith L. Meyer , Distinguished Research Professo r Emeritus, Odum School of Ecology, University of Georgia, Lopez Island, WA

Dr. Jana Milford , Professor, Department of Mechanical Engineering, University of Colorado, Boulder, CO

Dr. Christine Moe , Eugene J. Gangarosa Professor, H ubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA

Dr. Eileen Murphy , Manager, Division of Water S upply, New Jersey Department of Environmental Protection, Trenton, NJ

Dr. Duncan Patten , Research Professor , Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MTix Dr. Stephen Polasky , Fesler-Lampert Professor of Ecological/Environmental Economics, Department of Applied Economics, Univ ersity of Minnesota, St. Paul, MN

Dr. Stephen M. Roberts , Professor, Department of Physiol ogical Sciences, Dir ector, Center for Environmental and Human Toxicology, Univ ersity of Florida, Gainesville, FL

Dr. Amanda Rodewald , Associate Professor, School of Environment and Natural Resources, The Ohio State University, Columbus, OH

Dr. Joan B. Rose , Professor and Homer Nowlin Chair for Water Research, Department of Fisheries and Wildlife, Michigan St ate University, East Lansing, MI

Dr. Jonathan M. Samet , Professor and Flora L. Thornton Chair, Department of Preventive Medicine, University of Southern California, Los Angeles, CA

Dr. James Sanders , Director and Professor, Skidaway Institute of Oceanography, Savannah,

GA

Dr. Jerald Schnoor , Allen S. Henry Chair Professor, De partment of Civil and Environmental Engineering, Co-Director, Center for Global a nd Regional Environmental Research, University of Iowa, Iowa City, IA

Dr. Kathleen Segerson , Professor, Department of Economics , University of Connecticut, Storrs,

CT

Dr. V. Kerry Smith , W.P. Carey Professor of Economics , Department of Economics , W.P Carey School of Business , Arizona State University, Tempe, AZ

Dr. Herman Taylor , Professor, School of Medicine, Univer sity of Mississippi Medical Center, Jackson, MS

Dr. Barton H. (Buzz) Thompson, Jr. , Robert E. Paradise Professor of Natural Resources Law at the Stanford Law School and Perry L. McCarty Director, Woods Institute for the Environment, Stanford Un iversity, Stanford, CA

Dr. Paige Tolbert , Associate Professor, Department of Environmental and Occupational Health, Rollins School of Public Health, Emory University, Atlanta, GA

Dr. Thomas S. Wallsten , Professor and Chair, Department of Psychology, University of Maryland, College Park, MD

Dr. Robert Watts , Professor of Mechanical Engineer ing Emeritus, Tulane University, Annapolis, MD

SCIENCE ADVISORY BOARD STAFF Dr. Angela Nugent , Designated Federal Officer, 1200 Pennsylvania Avenue, NW 1400F, Washington, DCx 

### ACRONYMS

ACGIH TLV American Conference of Governmental Industrial Hygienists Threshold Limit

Values

AERMET AERMOD Meteorological Preprocessor

AERMIC American Meteorological Society/ Environmental Protection Agency Regulatory Model Improvement Committee

AERMOD AERMIC Dispersion Model

AMS American Meteorological Society

ATSDR MRL Agency for Toxic Substances and Disease Registry minimum risk levels

AEGL Acute Exposure Guidelines Limits

ANPRM Advanced Notice of Proposed Rulemaking

CalEPA California Environmental Protection Agency

D/F Dioxin and Furan

ERA Ecological Risk Assessment

ERPG Emergency Response Planning Guidelines

HAP Hazardous Air Pollutant

HEM-AERMOD Human Exposure Model - AERMIC Dispersion Model

HQ Hazard Quotient

IRIS Integrated Risk Assessment System

LOAEL Low Observed Adverse Effect Level

MACT Maximum Achievable Control Technology

MTBE Methyl Tertiary Butyl Ether

MIR Maximum Individual Risks

NATA National Air Toxics Assessment

NEI National Emissions Inventory

NESHAP National Emission Standard for Hazardous Air Pollutantsxi NIOSH National Institute of Occupational Safety and Health

NIST National Institute of Standards and Testing

NOAEL No Observed Adverse Effect Level

NPRM Notice of Proposed Rulemaking

NWS National Weather Service

OAQPS Office of Air Quality Planning and Standards

OSHA Occupational Safety and Health Administration

OEHHA Office of Environmental Health Hazard Assessment

PAH Polyaromatic Hydrocarbon

PB-HAP Persistent Bioaccumulative - Hazardous Air Pollutant

POM Polycyclic Organic Matter

REL Reference Exposure Levels

REM Refineries Emissions Model

RfC Reference Concentration

RfD Reference Dose

RTR Risk and Technology Review

SAB Science Advisory Board

SMAC Spacecraft Maximum Allowable Concentration

STEL Short Term Exposure Limit

TEQ Toxic Equivalents

TRIM.FaTE Total Risk Integrated Methodology - Fate, Transport and Ecological Exposure

TRV Toxicity Reference Values

TWE Toxicity Weighted Emissions

UCL Upper Confidence Limit

URE Unit Risk Estimates

USGS US Geological Surveyxii 

### TABLE OF CONTENT

1.0 EXECUTIVE SUMMARY .......................................................................................... 1

1. Revisions to emissions data ........................................................................................ 1 2. Dispersion Modeling ................................................................................................... 4 3. Dose-Response Assessment ........................................................................................ 5

4. Chronic Health Assessment ........................................................................................ 6

5. Acute Health Assessment ........................................................................................... 8 6. Ecological Risk Assessment ....................................................................................... 8

7. Risk Characterization .................................................................................................. 9

2.0 Background and Introduction ..................................................................................... 11

3.0 Response to Charge Questions .................................................................................... 14

Charge Question 1A ...................................................................................................... 14 Panel Response ............................................................................................................. 14 Charge Question 1B ...................................................................................................... 22 Panel Response ............................................................................................................. 23 Charge Question 1C ...................................................................................................... 24 Panel Response ............................................................................................................. 25 Charge Question 2 ........................................................................................................ 26 Panel Response ............................................................................................................. 27 Charge Question 3A ...................................................................................................... 31 Panel Response ............................................................................................................. 31 Charge Question 3B ...................................................................................................... 35 Panel Response ............................................................................................................. 35 Charge Question 4A ...................................................................................................... 37 Panel Response ............................................................................................................. 38 Charge Question 4B ...................................................................................................... 38 Panel Response ............................................................................................................. 39 Charge Question 5 ........................................................................................................ 42 Panel Response ............................................................................................................. 43 Charge Question 6 ........................................................................................................ 45 Panel Response ............................................................................................................. 45 Charge Question 7 ........................................................................................................ 49 Panel Response ............................................................................................................. 49

APPENDIX A -References that may be relevant to ecological risk assessment.............55

APPENDIX B -Editorial suggestions for risk characterization sections:.....................57

### 1.0 EXECUTIVE SUMMARY

This report was prepared by the Science A dvisory Board (SAB) Risk and Technology Review (RTR) Panel (the “Panel”) in response to a request by EPA’s Office of Air Quality Planning and Standards (OAQPS) to review their draft document entitled, “Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory

Board with Case Studies – MACT I Petrol eum Refining Sources and Portland Cement Manufacturing” (EPA-452/R-09-006, June 2009). This document (hereinafter referred to as the “Agency’s draft RTR document”) describes th e Agency’s proposed methods for assessing “residual risk” i.e., the risks remaining afte r application of maximum achievable control technology (MACT). The methods are demonstrat ed through the use of two case studies: (1) petroleum refineries and (2) Portland cement manu facturing facilities. The Panel reviewed the case studies to provide input on the RTR me thods and did not addr ess their regulatory implications.

The Panel deliberated on the charge que stions during a July 28-29, 2009 face-to-face meeting and discussed its draft report in a subsequent conference call on December 3, 2009. The Chartered SAB conducted a quality review of this document on March 24, 2010. The charge questions focused on seven topics within the Agency’s draft RTR document, including the derivation of emissions estimates, inputs for th e dispersion modeling, sel ection of dose-response values, estimating chronic inhalation exposures, developing estimates of acute inhalation risk, developing an ecological risk assessmen t, and overall risk characterization.

This Executive Summary highlights the Pa nel’s major findings and recommendations. The Panel commends the Agency on the technica l quality of the draft RTR document and the thought and effort it has put in to developing the residual risk methodology. The Panel found the case studies extremely valuable in illumina ting both strengths and limitations of the methodology. The issues involved in residual ri sk estimation are extremely complex and the

available information is limited. The comme nts and recommendations offered below are intended to assist OAQPS staff as they seek to improve their RTR assessments going forward, and are not meant to detract from the general ex cellence of the Agency’s draft RTR document or

the efforts to date.

1. 

### Revisions to emissions data

As described in Section 2.2.1 of the Agency’s draft RTR document (the Petroleum Refineries case study), the 2002 National Emi ssions Inventory (NEI) serves as the starting point for RTR assessments. EPA performs an engineering review of data from each source category to identify and correct readily apparent problems. The da taset is then publishe d through an Advanced Notice of Proposed Rulemaking (ANPRM), ma king it available for public comment. EPA evaluates comments and corrections for quality an d engineering consistency, revises the dataset, and develops a draft risk assessment. The data set and the risk assessment are provided with a Notice of Proposed Rulemaking (NPRM) for a second 60-day comment period, after which

1further comments and corrections are evaluated and incorporated, as appropriate. The final rulemaking is then developed.

Evaluations of petroleum refinery emissions estimates

The Panel notes that emissions data are one of the most critical i nputs to a residual risk assessment. The Panel agrees th at the overall approach describe d in Section 2.2.1 of the Agency document provides a consistent and well documen ted starting point for emission scenarios. However, the Panel is concerne d that the NEI, which reports estimates of actual emissions, may not be the most appropriate starting point for the RTR assessments, due to possible underestimation bias and the potential that em issions could be increased within current regulatory limits. The panel recommends that wh ere applicable, EPA start with facility-specific

allowable emissions to directly assess the effectiven ess of the current MACT standards. As a second step, risk estimates could also be develo ped using estimates of act ual facility emissions, such as those reported in the NEI (with corrections as appropriate), to estimate current risk in the community. The RTR case study focuses on this second issue, but does not adequately address

the concern that facilities can increase HAP emissions to MACT-allowable levels.

Overall, the Panel found the evaluations and comparative analyses described in Appendixes A, L and P to be informative and scientifically credible. Comparisons between alternative inventory estimation methods of the maximum individual cancer risks (MIR), cancer incidence and population exposure, hazardous air pollutant (HAP) emissions, and toxicity weighted HAP emissions are useful for illustrating the key uncertainties in the current approach. However, the overarching result that emerges fr om the evaluations is th e indication that some self-reported facility-specific emissions data in the NEI are either incomplete or biased low and that the comment and revision proce ss fails to correct this bias.

Appendix A compares risk assessment results for petroleum refineri es developed using the emissions data from the engineering review and using data revised following the public comment period. In both cases, th e analysis relies on reported emi ssions and does not identify or reflect further changes that may be needed to represent what MACT 1 petroleum refineries actually emit or are allowed to emit. Appendix A indicates that “facilities with a higher

maximum individual cancer risk in the ANPRM were more likely to provide data changes” (p. A-8) and that these changes genera lly reduced the risk estimates. To ensure balanced review, the Panel recommends that EPA expand its effo rts to encourage a nd assist community representatives to acquire releva nt information and pr ovide comments reflect ing their concerns.

Appendix L compares ambient benzene concentr ations with modeled concentrations for two petroleum refineries. The an alysis suggests the input emissi ons data may be biased low, although inappropriate treatment of calm periods in this modeling analysis could be contributing to the apparent bias. The Panel recommends expanding the assessment to include up to 15 randomly selected refineries (~ 10 % of the total) to better re present the distribution in error

across facilities. The current assessment could also be improved by better coupling of the measurements at the source and receptor and disc ussing the confidence in the inventory for both

facilities.

2Appendix P compares risk estimates devel oped using NEI-based emissions inventory data with estimates developed using a process-ba sed emissions model, the Refineries Emissions Model (REM). The comparison demonstrates differe nces in total emissions from refinery MACT 1 sources of a factor of almost 3 (undere stimation) for benzene and a factor of 50 (overestimation) for methanol. Estimated cancer incidence for the source category is 3-4 times higher using REM emission data relative to NEI-ba sed emissions estimates. The Panel finds this analysis particularly useful, as it most direc tly compares results based on reported NEI emissions

versus estimates based on MACT compliance or “allowable” emissions.



### Estimating dioxin and furan (D/F) e 

### missions

The Panel recommends that residual risk as sessments be conducted using the current source-specific National Emission Standard fo r Hazardous Air Pollutant s (NESHAP) allowable emission rate in combination with each facility’s maximum permitted production rate. This should be done whenever NESHAP emission limits have been set for specific hazardous air pollutants. This approach is recommended because it directly addresses the impact of the current emissions limits. In particular, using estimated emissions that exceed the NESHAP limit is not appropriate for the residual risk assessment. Because allowable limits were not modeled for dioxin and furan (D/F) emissions from Portland cement facilities, the Panel does not believe the approach used in the case study represents the best available me thodology in support of a residual risk analysis.

Additionally, the NESHAP compliance testing information for D/F emissions from each facility should be collected and critically evaluated to determine if it is te chnologically feasible to reduce the current Portland cement NESHAP D/F emission limits. This compliance information should be readily available upon reques t from the states or EPA regional offices.

This should be done whenever NESHAP emission limits have been set for specific HAPs. In contrast, use of the 95% Upper Confidence Limit (UCL) of available actual data as a default emission rate estimate may be appropriate for 1) source categories that do not have a NESHAP emission limit for D/F, and 2) all other HAPs th at do not have a current NESHAP emission limit.



### Estimating emissions of radionuclides

Emissions of isotope-specific radionuclid es warrant careful characterization and

evaluation for Portland cement facilities and other f acilities with the potential to emit relevant radionuclides. However, the proposed analysis should not be formally included in the RTR assessment until further progress is made to qua ntify the isotope-specific radionuclide emissions and the associated risks. The Agency’s analysis demonstrates that isot ope-specific radionuclide

emissions estimates are needed instead of using 2002 NEI data that do not include such speciation.

The radionuclide content of feedstocks used to produce Portland cement should be characterized at important loca tions across the US where these feedstocks are mined. With information on radionuclide content of feedstocks, screening material bala nce calculations such as those done by Leenhouts et al. (1996) for the Maastricht faci lity should be performed to

3estimate isotope-specific radionuclide emissions. Re sults from radionuclides stack tests required for compliance assurance may also provide useful data.

2. 

### Dispersion Modeling

Section 2.2.2 describes the Agency’s inputs to the AERMOD dispersion model for RTR assessments. The Agency performed several sensitivity analyses to better understand the uncertainties and/or potentia l bias that may be introduced by some of these inputs. The Panel believes that the dispersion modeling for primary HAPs used in risk assessments is well developed and appropriate. Any modeling entails uncertain ties, and the series of case studies presented in Section 4 of the Agency’s draft RTR document provide a broad picture of model performance and sensitivity for this risk assessment. The Agency has presented calculations justifying the use of several simplifications for performing longer-term impact and risk assessments. Some simplifications were show n to introduce relatively minor changes most of the time. However, other simplifications intr oduced changes in risk estimates that could be appreciable, and in other areas fu rther investigation is required in order to adequately justify the conclusions. In particular, it a ppears that there is a potentially serious underestimation bias in the dispersion modeling due to the ambiguous treat ment of “calm” periods that have no definable

wind directions.

The Panel noted that the choice of meteorol ogical data for performing risk assessments appears to have a significant imp act on calculated risks, as demonstr ated in the sensitivity studies presented in section 4.5. In partic ular, use of meteorological data from stations far removed from the source facilities introduces significant uncerta inty; local data should be used where possible. The Panel also suggests that use of more than one year of meteorol ogical observations is desirable in order to capture wo rst-case scenarios. The methods for choosing an individual year for risk assessment suggested here could be a pplied to other source categories, but depending on source stack characteristics, some of the quant itative conclusions of the Agency’s sensitivity studies may not transfer.

The results of the Agency’s analysis of omitting HAP decay and deposition in risk assessments do support this practice, which c ould be applied to other source categories. However, it is possible that secondary HAP formation through atmospheric chemical reactions could be significant for some source categories. Further sensitivity studies of secondary HAP formation would be required to rule out th e necessity of including complex photochemical modeling for future HAP risk assessments.

In order to correctly assess whether consideration of imp acts at census block centroids reasonably assesses risks at actual residences within census blocks, the HEM-AERMOD system should be run twice with different sets of recep tors: (1) census block centroids, and (2) specific

locations of residences. Maximu m health risk impacts would be directly compared using these two sets of receptors for a numbe r of facilities. It is possible that differences between block

centroids and individual residences could be greater than the differe nces shown in this sensitivity study for source categories that are characterized by elevat ed buoyant emissions from

smokestacks.

4

### 3. Dose

­ Response Assessment

Section 2.2.6 of the Agency’s draft RTR docum ent describes the process of selecting and prioritizing dose-response values for RTR human health risk assessments. The Agency selected chronic dose-response values in the same way it does for the National Air Toxics Assessment (NATA), a process the SAB has already reviewed. The Agency has also developed an analysis (Appendix O) of the possible impor tance of HAPs that lack chroni c dose-response values. This analysis suggests that only a few HAPs lacking such values could be important in the chronic risk assessment, with the degree of importan ce heavily dependent on the conservatism of the input assumptions.

The Agency developed its selection process fo r acute dose-response values more recently than the one for chronic values, and it has not yet undergone SAB review. The acute risk assessment process must deal with more gaps and inconsistencies in health benchmarks, compared to the chronic risk assessment.



### Selecting and prioritizing chronic dose

­ response 

### values

The Panel found the approach used in the RTR assessments was reasonable, but too simplistic in that it accepts dose-response numbers at face value, without closely examining the quality or validity of the value(s) chosen. To assist in comparing alternative chronic toxicity values, the Panel recommends that a table be created, including all the chemicals under consideration and all of the elig ible dose-response values, along w ith the source of the value, the year it was last updated, and a qualitative descript ion of the effect. If the chronic dose-response values are significantly different, especially if th e value is a driver for the risk assessment, a review should be conducted to understand why th e values differ, with professional judgment used to select values for the assessments. If a chemical for which dose-response values have not been updated recently appears to be a risk driver , a literature search s hould be performed to identify studies that may revise the value and the chemical should be considered for addition to the Integrated Risk Assessment System (IRIS) high priority revision list.

The preferred database for chronic dose-response data is and should be the IRIS database. However, some chemicals of interest do not have IRIS values, and values for other chemicals have not been reviewed recently. The Panel urges the Agency to address these gaps and provide the resources necessary to mainta in the updating process. Additional sources of data may also be considered if they have undergone adequa te and rigorous scientific peer review.

The Panel recommends that the Agency expa nd the methods discussion in Appendix O to better describe the toxicity we ighted emissions (TWEs) estimates for chemicals having no unit risk estimates (UREs) or refere nce concentrations (RfCs). In addition, the discussion of how surrogates were chosen should be clarified. Li mitations of the emissions data need to be

identified and addressed. The Panel recommends that the Agency prepar e or compile toxicity profiles for each of the HAPs that Appendix O iden tifies as having the pote ntial to drive the RTR

assessment.

5The Panel was not charged with critiquing the IRIS methodology itself, however, we note

that inhalation risk methods for children are st ill developing and that Ca lifornia’s Office of Environmental Health Hazard Assessment (OEH HA) has very recently updated its methodology in ways that could affect th e development of RfC and URE values. EPA should examine these developments to make sure that the RTR pro cess adequately covers children's risks.



### Selection of acute benchmark 

### values

The case studies characterize acute risk ade quately, but this may be due to their unique attributes; thus, there is a need to pay atten tion to the principles and practices used. The incorporation of the available California Reference Exposure Leve ls (RELs) for the assessment of acute effects is a conservative and acceptable a pproach to characterize acute risks. The Panel supports EPA’s decision not to use ATSDR acute Minimum Risk Levels (MRLs) as a basis for developing acute benchmark values because of th eir temporal mismatch; developing a correction for this mismatch would require formal peer review. The Panel has some concern with the use of the Acute Exposure Guidelines Limits (AEGLs ) and Emergency Response Planning Guidelines (ERPGs). When AEGL-1/ERPG-1 emergency gui deline values must be used, the Panel recommends adjusting them by a factor of 3 if the value is based on a LOAEL ( Lowest Observed

Adverse Effect Level ) rather than a NOAEL (No Observed A dverse Effect Level). AEGL-2 and ERPG-2 values should never be used in residual risk assessments because they represent levels

that if exceeded could cause se rious or irreversible health effects. Spacecraft Maximum Allowable Concentrations (SMAC) for Selected Airborne Contaminants could be considered, with appropriate adjustments to account for the need to protect sensitive subpopulations. When more reliable information is not available, American Conference of Governmental Industrial Hygienists Threshold Limit Values (ACGIH TLVs) could also be considered for use in the risk assessments, with appropriate adjustment to ensu re the protection of sensitive sub-populations. TLV values should only be used af ter thorough and critical evaluation.

As recommended for chronic dose-response va lues, all the acute values for a given chemical should be arrayed in a table that displa ys their similarities and differences. Expert judgment should then be applied to select the mo st appropriate value with a clear rationale for

the selection. Care must be exercised to ensure that the value chosen has undergone appropriate peer-review.



### 4. Chronic Health Assessment

Section 2.2.3 of the Agency’s draft RTR doc ument describes the process by which the Agency estimated chronic human inhalation exposures based on modeled average ambient

concentrations at census block centroids. For these case studies , this process did not include consideration of either daily behavior patter n or long-term migrati on behavior. Section 2.2.3 presents a rationale for omitting daily behavior , and Appendix N presents a case study that adjusts inhalation-based lifetime cancer risk estimates for indivi duals to account for long-term migration.

6For persistent and bioaccumulative HAPs (PB-HAPs), the Agency’s draft RTR document describes a two-step approach. As described in Appendix C, the TRIM modeling system is first used to develop what the Agency calls “ de minimis emission rates” such that emissions below these levels should not produce unacceptable risks in reasonable worst-case conditions. Facilities emitting PB-HAPs at higher rates mi ght require refined multi-pathway modeling, as illustrated in section 3.4 and Appendix I in a case study of a Portland cement facility.



### Estimating Inhalation exposures

In general, EPA’s overall approach appears to be reasonable as a screening approach for localized impacts that can be re fined if needed in individual cases. However, an overarching concern with the Agency’s chronic inhalation ex posure estimates is that children’s exposures do not appear to have been adequately addressed. With regard to the chronic inhalation exposure estimates, the Panel finds the rationale for omitti ng daily behavior to be convincing. Given the age of some of the available activity pattern data and the inherent community-scale activity pattern uncertainties between locat ions, the decision to omit daily be havior is justified. The Panel further recommends that long-term migration not be incorporated into the ri sk assessment at this

time. It does not add value to the risk asse ssment and introduces addi tional uncertainty.



### TRIM model as a screening tool

In responding to this charge question, the Panel considered how TR IM.FaTE results were applied in the risk assessment process, but did not evaluate th e details of the equations in TRIM.FaTE nor evaluate the validity of the mode l. With the caution that continued efforts are needed to evaluate the TRIM.FaTE model, the Panel finds that the Agency’s screening approach is based on an appropriate framework and should us efully screen out sources that do not need a detailed site-specific multi-pathway analysis. The screening-level multi-pathway assessment is thorough and conservatively include s local subsistence agricultural and fishing scenarios, adding exposures across intake pathways to yield total PB-HAP exposure.

While the Panel supports the Agency’s sc reening approach, we recommend EPA avoid using the term “ de minimis ” to describe the threshold emi ssions estimates it has derived. In particular, when the background concentration of a PB-HAP al ready exceeds a safe level (e.g., where a fish advisory is alre ady in effect) the public may not understand a local source’s contribution being characterized as de minimis . Furthermore, the model results should be clearly presented to show 1) the relative fraction of th e source’s emissions that are deposited locally versus being transported to add to regional burd ens, and 2) the relative contributions to total multi-pathway exposure from local and regional background sources.

Given the current status of in formation on radionuclide emissi ons, the Panel agrees it is acceptable to omit them from the multi-media as sessment. However, EPA should work towards including them, as non-inhalation pathways are often important for radionuclides that can accumulate in biota and subsequently be ingested.

7

### 5. Acute Health Assessment

Section 2.2.5 of the draft RTR document desc ribes the Agency’s process for developing screening and refined estimates of acute inhalation risk. For acute screening purposes, the Agency has assumed that, in the worst case, a pe rson could be exposed for one hour to ten times the highest hourly concentration calculated by the disper sion model. This in effect assumes a 1- hour emission rate of ten times (10X) the annual average (assuming continuous emissions), with

simultaneous occurrence of “wor st-case” meteorological conditions, and the presence of a person

at this worst-case downwind location.

The Panel agreed there is a critical need fo r better data addressing short-term exposures to HAPs. The Panel also agreed that in the abse nce of chemical- and site-s pecific data, the use of the 10X screening assumption for petroleum refineries seems reasonable. While hourly emissions may be much higher than 10X annua l averages, the highest excursions would infrequently occur together with worst case expo sure conditions. However, the methods used to derive and justify the 10X screening assumpti on need to be more clearly presented. For petroleum refineries, the Panel al so suggests that following the sc reening process, the chemicals of highest concern be compared with the list of chemicals reported in the Houston area (Appendix B), to ensure they are adequately re presented. Although the Panel generally agreed that the 10X assumption could be used for othe r geographic areas, it was felt that the actual releases would depend on the ma nufacturing processes involve d, which may or may not be captured in the Houston example.

The Panel also recommends that the Agency examine the likelihood that a 10X release would occur under the most hazardous meteorological c onditions, and how likely it would be for 10X releases of multiple chemicals to occur simulta neously. If it is concluded that simultaneous releases under adverse meteorological conditions would be very unlikely, then summing the acute hazard quotients by target organ would not be necessary.



### 6. Ecological Risk Assessment

Section 3.5 and Appendix J of the Agency’s dr aft RTR document describe a refined, sitespecific application of TRIM to conduct an ecological risk assessment for PB-HAPs emitted by the same Portland cement facility evaluated in the human health risk assessment. Appendix J also describes a nationwide facility ranking exer cise that identifies Portland cement facilities with the highest potential for causing indir ect ecological effects via acidification of the environment by hydrogen chloride emissions. A ppendix K describes an analysis of possible direct effects on plant foliage of air concentr ations of hydrogen chloride emitted from Portland

cement facilities that are below human health thresholds.

The Panel found the ecological ri sk assessment (ERA) presented in Appendix J to be an impressive effort, but one that needs improvement to better follow the Agency’s ERA guidelines. The heavy reliance of the ERA case study on TRIM .FaTE is a concern, as this model has not been well validated in the peer-reviewed literatu re for ERAs, and lacks an adequate sensitivity analysis with ground-truthing. Overall, many of the Panel’s concerns and issues with the

8ecological risk assessment could be addresse d by conducting a ground-truthing ERA at a site such as Ravena Pond, or by a comparison of TR IM.FaTE predictions with more conventional

ERA methods.

The Panel recommends EPA further investigat e the numerous peer-reviewed studies that are relevant to this process, many of which have focused on mercury and highly chlorinated compounds such as dioxins. In Appendix J, sect ion 3.2.3, EPA discusses and rejects the option of using Toxicity Reference Values (TRVs) expresse d in terms of tissue concentrations instead of chemical intake. However, reporting TRVs in te rms of tissue concentrations (rather than intake as commonly done for human risk assessments) would allow for more and better comparisons with the peer-reviewed literature and predicti ons of risk, as there are fewer peer-reviewed literature reports of intake values.

The Panel found that the process used to sel ect the Portland cement facilities of greatest potential concern for HCl deposition using pH, ha rdness, alkalinity and soil type data was very good. However, it is important to recognize that fo r site-specific ERAs, other site characteristics may need to be considered



### 7. Risk Characterization

The risk characterizations for the two case st udies (Sections 2.3 and 3.6 of the Agency’s draft RTR document) represent the Agency’s cu rrent practices in providing information to decision-makers responsible for RTR rulemaking. The analyses presented in the appendices are by and large illustrative of what can currently be done in the regulatory context, given knowledge, time, and resource constraints.

The Panel believes that the authors of the Agency’s draft RTR document took great care in summarizing and providing justification and explanation for most of the results, including attention to uncertainties. However, a number of improvements are possible. In the RTR case studies, the presentation of me thods, risk assessment results, and risk characterization are intermingled, such that the purposes of the risk characterization are not met. This can be improved by focusing more on the need to comm unicate with decision makers as the primary audience, recognizing that transparency is impor tant and that the audience will inevitably be

broad. While other sections of the RTR assessmen ts should document the technical details, the

risk characterization sections should stand alone. To this end, the Panel recommends that EPA develop a separate methods document that contains a full description (inc luding uncertainties) of all of the common components of the source-specific risk assessments. Source-specific risk

characterizations could refer back to this master document, while providing additional information particular to the source category at issue.

Decision makers and communities need to understand the broad community risk and contributors to it. However, because the Clean Air Act requires separate assessments by source category, EPA’s RTR approach onl y partially accounts for potential human health or ecological

risk of facilities that fall into more than one category. For example, the petroleum refinery MACT 1 case study omits refineries’ combustion processes. The risk characterization should

9clearly explain this limitation. Furthermore, the ri sk characterization should put the results in the broader context of aggregate a nd cumulative risks, including background concentrations and

contributions from other sources in the area.

While recognizing that RTR assessments mu st proceed, even though most will have a relatively long list of uncertainties, the Panel reco mmends that the Agency perform a sensitivity analysis to identify the major uncertainties in both the human health and ecological risk assessments. The Agency should then proceed to: (1) explain them clearly in the risk characterization section and (2) take steps to reduce them.

10

### 2.0 Background and Introduction

EPA’s Office of Air Quality Planning and St andards (OAQPS) requested that the Science Advisory Board (SAB) review their draft document entitle d, “Risk and Technology Review (RTR) Risk Assessment Methodologies: For Revi ew by the EPA’s Science Advisory Board: Case Studies – MACT I Petroleum Refining S ources Portland Cement Manufacturing” (EPA452/R-09-006, June 2009). This document, hereaf ter referred to as the Agency’s draft RTR document, describes the Agency’s draft me thodologies for conducting Risk and Technology Review assessments. As required by the Clean Air Act, these assessments ev aluate the effects of

industrial emissions of hazardous air pollutants (HAPs) on public he alth and the environment. The proposed methodologies are demonstrated thr ough the use of two case studies, (1) petroleum refineries and (2) Portland cement manufacturing facilities.

The Clean Air Act establishes a two-stage re gulatory process for addressing emissions of HAPs from stationary sources. In the first stage, the Act requ ires EPA to develop technologybased standards based on Maximum Achievable Control Technology (MAC T) for categories of

industrial sources. EPA must review each MACT st andard at least every eight years and revise them as necessary. In the second stage of the pr ocess, EPA is required to assess the health and

environmental risks that remain after MACT has been applied. EPA must develop standards to address these remaining risks if necessary to protect the public h ealth with an ample margin of safety or to prevent adverse environmental effect s. This second stage of the process is known as the residual risk review, and must be completed w ithin eight years of promulgation of the initial MACT standards for each source category.

In order to streamline and standardize the re sidual risk review fo r the large number of source categories at issue, EPA has devel oped a process by which it (1) conducts a risk assessment using currently available source and emissions data; (2) requests public comment on the source and emissions data, along with pr eliminary risk assessmen t results, through an Advance Notice of Proposed Rule Making (ANP RM); (3) addresses comments received on the ANPRM; and (4) revises the risk assessment as needed. The results of the revised risk assessment are intended to support proposals and promulgation of technology- and risk-based regulatory decisions through no tice-and-comment rulemaking.

Previous SAB panels and other internal Agen cy and external peer review panels have reviewed aspects of the RTR methodology, as documented in the following reports:

1) The Residual Risk Report to Congress , a document describing the Agency’s overall analytical and policy approach to setting residua l risk standards, was issued to Congress in 1999 following an SAB peer review. Many of the design features of the RTR assessment methods were described in this report, alt hough individual elements have generally been improved over the techniques described in that document. (available at: http://www.epa.gov/ttn/oar pg/t3/reports/risk_rep.pdf )

2) Individual residual risk assessm ents – several internal peer reviews and one external peer

review were conducted on risk assessments fo r individual source categories, including Coke Ovens ( http://www.epa.gov/ttn/atw/coke/coke_rra.pdf ), Perchloroethylene Dry Cleaning

11( http://www.epa.gov/ttn/atw/dr yperc/11-14-05riskassessment.pdf ), and Halogenated Solvent Cleaners (downloadable from: http://www.epa.gov/ttn/a tw/degrea/halopg.html ). Each of

these assessments used emission estimates fr om the National Emissions Inventory (NEI), human exposure modeling at the census block level, dose-response me thodologies, and risk characterization that are similar to those for the planned RTR assessment.

3) The National Air Toxics Assessment, or NATA, for 1996 was peer-reviewed by an SAB panel in 2001-2002 (the SAB peer review report is available at: http://yosemite.epa.gov/sab/sabprodu ct.nsf/214C6E915BB04E14852570CA007A682C/$File /ecadv02001.pdf ). NATA 1996 was a comprehensive and cumulative risk assessment designed to include all mobile sources, small industrial sources, and large industrial sources, as well as background contributions of air toxi cs. Because of significant uncertainties, the SAB did not believe that it was appropriate for regulatory purposes. The assessment at that time did not carry a census block-level resoluti on, but rather was perf ormed at the census

tract level. For this reason, on EPA’s NATA website ( http://www.epa.gov/ttn/atw/natamain/ ), the estimated risks are characterized as "starting points" for developing refined assessments.

4) AERMOD, a recently-developed source-to-receptor air quality dispersion model, was the subject of significant interagenc y cooperation and peer review. It is now EPA’s preferred local-scale air dispersi on model for industrial sources of air pollution. ( http://www.epa.gov/scram001/di spersion_prefrec.htm#aermod )

5) The individual dose-response assessment va lues used in the RTR assessment have themselves been the subject of peer review s through the agencies that developed them (including EPA, through its Integrated Risk Information System, or IRIS; the California Environmental Protection Agency, or CalEPA, and the Agency for Toxic Substances and Disease Registry, or ATSDR). EPA proposes to select dose-response values for long-term exposures from these sources in the same priority order it used for NATA ( i.e. , IRIS, then ATSDR, then CalEPA). For acute exposure to xicity, EPA arrays several indices without prioritization. This area is a s ource of significant, usually unqua ntifiable uncertainty. (IRIS - http://cfpub.epa.gov/nc ea/iris/index.cfm , ATSDR - http://www.atsdr.cdc.gov/mrls/ , CalEPA - http://www.oehha.org/air/to xic_contaminants/index.html )

6) An earlier peer review of multi-pathway risk assessment methodologies was conducted by the EPA’s SAB in 2000. The final SAB advisory is available at: http://yosemite.epa.gov/sab/sabpr oduct.nsf/1F1893E27059DB55852571B9004730F7/$File/e cadv05.pdf

.

Of particular relevance to the current re view, a prior SAB panel provided a formal

consultation on the proposed RTR Assessment methodologies in June 2007. 1 OAQPS revised its process to incorporate many of the SAB panel’s suggestions, added significant new analysis

1 EPA-SAB-07-009 (2007), Available at the following URL: http://yosemite.epa.gov/sab/sabproduct.nsf/02ad90b136fc21ef85256eba00436459/33152C83D29530F08525730D00 6C3ABF/$File/sab-07-009.pdf

12and methods, and developed illustrative risk a ssessments based on the revised methodology. T

current review examines the revised and e xpanded methodology, as il lustrated through case studies for the petroleum refining a nd Portland cement source categories. he

The Risk and Technology Review (RTR ) Methods Panel met through a public

teleconference call on June 30, 2009 for a briefing on EPA’s Risk and Technology Review methodology and to review the charge questions pr esented by the Agency. The Panel then met in a public meeting on July 28 – 29, 2009 in Research Triangle Park, NC, to review the Agency’s draft RTR document. The Panel held a subseque nt teleconference call on December 3, 2009 to discuss its draft advisory re port. The Chartered SAB conducte d a quality review of this document on March 24, 2010. The responses that are contained in this report represent the views of the Panel. The specific charge questions to the Panel are pres ented in the next chapter, along with the Panel’s responses.

13

### 3.0 Response to Charge Questions

Charge Question 1A

As described in Section 2.2.1 of th e Agency’s draft RTR document ( i.e., the Petroleum Refineries case study), the 2002 National Emissions Inventory (NEI) serves as the starting point for RTR risk assessments. EPA performs an engineering review of data from each source category to identify and correct r eadily-apparent limitations and i ssues with the emissions data. The dataset is then published through an Adva nced Notice of Proposed Rulemaking (ANPRM), making it available for public comment. EPA evaluates comments and corrections for quality and engineering consistency, revi ses the dataset, and develops a draft risk assessment. The dataset and the risk assessment are provided w ith a Notice of Proposed Rulemaking (NPRM) for a second 60-day comment period, after which furt her comments and corrections are evaluated and incorporated. The final rulemaking is then developed. We have attempted to assess the quality of this proc ess in three ways.

• Appendix A contains a comparison of risk esti mates based on EPA’s initial inventory as amended by engineering review and risk estimates based on th e inventory as revised by public comment.

• Appendix L contains a comparison of modele d and monitored benzene concentrations around two petroleum refineries, with the in tent showing if benzene emissions from refineries may have been underes timated at these facilities.

• Appendix P contains compares petroleum refine ry emissions estimates and facility risk estimates using the current RTR process to emission and risk estimates from the same facilities derived using a model plant appro ach based on generic emission factors. The goal of this analysis was two-fold: 1) to develop a bounding estimate regarding the potential underestimation of emissions in our baseline emissions dataset; and 2) to provide an indication of how much risk es timates might change based on this potential

underestimation.

1A. Do these comparisons provide useful information about the quality of the emissions data, and ultimately the risk estimates? Can you suggest improvements to these analyses, or others that might be more useful? Should we use th ese results to revise our risk assessment for petroleum refineries? Given that we have re latively high confidence about benzene emissions from refineries, can you suggest ways that we can develop similar analyses for other HAPs and source categories?



### Panel Response

Emissions data are one of the most critical inputs to a residual risk assessment. The process for deriving emission factors for the risk and technology review (RTR) risk assessments begins with the 2002 National Emi ssions Inventory (NEI) data compiled for individual facilities

14in a given source category. The data are reviewed and revise d by EPA (engineering review) followed by a two-stage public comment proce ss (ANPRM and NPRM) leading to further revisions in response to comments. EPA has invested a great deal of effort into adapting and applying the existing NEI data to construct em issions scenarios for the RTR assessments. The Panel agrees that the overall approach descri bed in Section 2.2.1 of the Agency’s draft RTR document provides a consistent and well documente d starting point for emission scenarios based on an existing and well documented data set. Ho wever, the Panel is concerned that the NEI, which reports estimates of actual emissions, may not be the most appropriate starting point for developing emissions data for the RTR assessments , due to possible underes timation bias and the potential that emissions could be increased within current regulatory limits. Where applicable, the Panel recommends that facility-specific allowable emissions be considered as a first step, to directly assess the effectiveness of the current MACT standard s. As a second step in the analysis, estimates of actual emi ssions could be used to estimat ed current risks. Continuing efforts are needed to reduce errors and uncertainty in these emissions estimates.

EPA performed three modeling an alyses for the petroleum refi neries case study to assess the quality of the process for developing RTR em issions data. The first analysis (Appendix A) compares the outcome of the risk assessment us ing emissions data from before and after the comment period to explore how the public co mment process influenced the outcome. The second (Appendix L) compares modeling result s for benzene concentrations to monitoring

results at two facilities to determine if em ission factors may be underestimated. The third (Appendix P) compared the current approach to a category specific emissions modeling approach using generic emission factors to explore the potential for underestimation of emissions in the base-line scenario and how this might influence risk estimates.

Overall, the Panel found the analyses desc ribed in Appendixes A, L and P to be informative and scientifically credible. Compar isons in the analyses such as the maximum individual cancer risks (MIR ), cancer incidence and populatio n exposure, HAP emissions, and toxicity weighted HAP emissions are useful for illustrating the key uncertainties in the current approach. However, the overarching result that emerges from the evaluations is the indication that some self-reported facility sp ecific emissions data in the NEI ar e either incomplete or biased low and that the comment and revision process fails to correct this bias. 2

It is the Panel’s understanding that the Agency is aware of the deficiencies in the petroleum refineries emission estimates. The Ci ty of Houston recently submitted a request for correction of information under the Data Qual ity Act and EPA’s Data quality guidelines 3 . The request cites reports of underestimation of em issions by up to two orders of magnitude for refineries and chemical manufacturing plants. The EPA responded in a letter 4 dated April 7, 2009, expressing concurrence with the City’s concerns and acknowledging the inaccuracy and uncertainty of emission estimates in the inventor y, particularly where there is heavy reliance on emission factors in the NEI. The Agency outline d a number of specific tasks that are currently on-going to address and fully understand this uncert ainty. The planned outcome of this work, as

2 See also Document ID EPA-HQ-OAR-2003-0146-0010, "Potential Low Bias of Reported VOC Emissions from the Petroleum Refining Industry", EPA Technical Memorandum from Brenda Shine, July 27, 2007

. 3 http://www.greenhoustontx.gov/reports/epaletter20080709.pdf

4 http://www.greenhoustontx.gov/reports/dataquality20090407.pdf

15described by the EPA, is to use the results of the emission factor verification project to help: a) evaluate risk to exposed populat ions; b) conduct comparisons to existing emission estimates (e.g. TRI) for specific facilities; and c) better char acterize the cost effectiveness of controls. The Panel is concerned that any residual risk decision made for the petroleum refinery source category without the use of this updated and verified emissions information would be premature.

The Panel’s review of the appendices is di scussed below followed by recommendations for improving the emission estimates for the RTR process.



### Appendix A: The stated purpose of Appendix A is to compare the risk assessment results using the emissions data from the engineer ing review with results using emission data that were revised following the public comment period. In addition to changes in the emissions data, a number of other changes were made to the risk assessment between the two cases. For example, Appendix A indicates that a lthough the same risk assessment model (HEM3/AERMOD) was used in both assessments, several updates were made in the version used with the post-comment emissions data. Specifically, the meteor ological data included additional meteorological stati ons and a newer version of th e AERMET model was used along with meteorological data from different (more recent) years. In addi tion, updated dose-response data were used for the post-comment assessment. The appendix is silent on the potential impact of these changes relative to changes in the emi ssions data. Although it is likely that emissions are the dominant factor influencing th e changes in the results, the validity of this assumption is not

demonstrated.

The comparison in Appendix A is focused on reported actual emissions. Thus the assessment does not identify or reflect further ch anges that may be needed to represent what MACT 1 petroleum refineries actually emit (as oppos ed to what they report emitting) or what they might emit if emissions were increased to allowable levels under existing MACT standards. The analysis would be more informative if it in cluded adjustments to the HAP emissions from all facilities needed to reflect representati ve emissions across the source category.

Another important observation from Appe ndix A is the relationship between the likelihood of receiving input during the public comment period and the magnitude of the individual risk values reporte d in the ANPRM. Figure 6 of Appe ndix A highlights the fact that comments were more likely to be provided for facilities for which individual cancer risk was relatively high and that these comments generally reduced the risk estimates. There is clear incentive for facilities associated with higher risk to offer corrections to the NEI data but it is unclear whether similar incentives are present to help identify underrepo rting facilities. The analysis would have benefited from a summary of the source of information received during the comment period to evaluate whether the commen ts originating from groups representing the facilities are generally balanced with comments from groups repres enting the community, or if facility-specific emissions data were submitted by state and local air pollution agencies. In many cases, community representatives might not ha ve the expertise or access to emissions information to provide substantive input to the review process. Most state and local air pollution agencies rely on the emission factors contained in EPA’s AP-42 Fifth Edition Compilation of Air

16Pollutant Emission Factors, Volume 1: Stationary Point and Area Sources 5 to estimate facility emissions unless they have facility specific emi ssions testing data. To ensure balanced review, the Panel recommends that EPA expand its e fforts to encourage and assist community representatives to acquire releva nt information and pr ovide comments reflect ing their concerns.



### Appendix L : The Panel recognizes that evaluati ng model performance using empirical observations is very important for increasing confidence in model-based assessments. In this Appendix, ambient benzene concentrations measur ed at two sampling locations were compared

to modeled concentrations at or near the same sample locations for two facilities as a way of assessing the emissions data used in the risk assessment at these facilities. The assessment

assumes that the dominant source of variation in modeled concentrations at the sample locations

is the emissions data used in the model runs. The Appendix shows that modeled concentrations are significantly lower than monitored concentr ations with the differe nce for one facility (Marathon facility) being much greater than the other. Th e Agency’s draft RTR document points out that a statistically significant difference do es not necessarily imply practical importance. However, the analysis clearly shows both an appa rent low bias in the emissions data and a low precision in the predictions from the two facilities. The analysis thus suggests the emissions data may be biased low, although inappropriate treatment of calm periods in this modeling analysis could be contributing to the apparent bias.

While the model results suggest th at emissions are biased low, it is notable that the results for the two facilities are very different. Annual averaged modeled concentrations are within 11% of the corresponding monitored values for the BP facility, but only within 72% for the Marathon facility. Correspondingly, the absolute errors between the measur ed and modeled annual average concentrations are 0.5 μg/m 3 and 3.4 μg/m 3 for the two petroleum refineries. Given that the

1x10 -6 cancer risk benchmark for benzene is an annual average concentration of 0.128 μg/m 3 , the

absolute error is considerable. The difference in error between the two re fineries highlights the problem with using a small sample size (n=2 out of 154 refineries) to assess model performance. The small and co-located sample of two facilitie s makes it difficult to conclude that a high level

of confidence exists in the eval uation of benzene emissions base d on these results. Furthermore, the analysis depends on extensive assumptions about averaging of emissions, characterizing surface roughness, and characterizing the mete orology. The comments offered by the internal

EPA reviewer about difficultie s in characterizing wind speed and direction closer to the receptors, and not including emissions from add itional sources (e.g., ship/barge traffic) are appropriate and may limit the value of this assessment.

Monitored ambient concentrations represent the sum of contri butions from all sources. In order to estimate the portion of the ambient concentr ation that could be attr ibuted to the source category or the specific facility, EPA used th e following general methodology: 1) monitors in close proximity to the source were used, 2) data were evaluated by wind dire ction so that it could reasonably be assumed that concentrations at th e monitor were related to the source (when the monitor was downwind from the source) and, 3) con centrations not attributed to the source (e.g., on-road mobile, background estimated from NATA) we re subtracted from the total concentration seen at the monitor. The background estimate app ears to be a type of correction factor and an

5 AP-42 Fifth Edition Compilation of Air Pollutant Emission Factors, Volume 1: Stationary Point and Area Sources. Available On-Line: http://www.epa.gov/ttnchie1/ap42/

17attempt to account for the contributions from ot her sources. It is unclear where the background estimate came from and/or if dispersion modeling was used to derive it. It may be that the background value is actually a crude combinati on of unaccounted for fugitive emissions and error from under reporting in the emission inve ntory. Because the background may reflect errors in reported emissions, it may not be appropriate to subtract this source off hand from the ambient

concentrations. While it is important to account for backgrou nd given the long half-life of benzene, the analysis should provide a better description of the bac kground estimate, including

where it comes from and its spatial distribution.

The choice of using meteorology from the mo re distant site (Galveston) when local

information was available seems incorrect. Ordina rily the closest meteorological monitor should be utilized. The model-to-data comparison in this appendix needs to be appropriately adjusted under the assumption that a potenti ally significant error could ha ve been introduced into these comparisons by using incorrect meteorology. The fact that there is general agreement of the plume positioning with wind directi on suggests that the winds in Ga lveston statistically resemble the winds further inland at the refinery lo cation, but hour-by-hour discrepancies may be significant. Although clustering of sites that behave in a similar manner is seen farther up the ship channel, the Galveston airport site is like ly to act more independe ntly given its location. This site is open and closer to the Gulf. Uncer tainty in the wind dire ction and speed could be brought into the model and spatially assessed. EPA defends its use of data from the Galveston airport site by pointing out questions in the repr esentativeness of the Texas City Ball Park site,

which is closer to the refineri es. Re-evaluation which includes a margin of error is the only way

to ascertain the influence of the issues with the wind data. An additional examination of the model to monitor comparison for these two facili ties using the closer me teorological data set

would be useful.

The assessment could also be improved by bett er coupling of the measurements at the source and receptor and discussing the confidence in the inventory for both facilities. This would strengthen the analyses. From the background documentation contained in the Air Docket (EPAHQ-OAR-2003-0146), it appears that the BP-Texas City facility has pr ovided a credible assessment of their inventory based on the li mited model to monitor comparisons and the findings of the 22 facility study that indicated BP-Texas City seemed to properly account for benzene emissions from their storage tank f acilities in comparison to other facilities. 6 The

confidence in the inventories for the two facilitie s could also be discus sed in light of other findings from the 22 facility study, such as the finding that many facilities underestimate their

benzene emissions from the wastewater stream by as much as a factor of 40 to 1400.

The Panel recommends expanding the assessment to include up to 15 randomly selected refineries (~ 10 % of the total) to better represen t the distribution in error across facilities. It is unlikely that the discrepancy between reported and actual emissions can be assumed to be constant between facilities. To gain a better understanding of the modeled to measured error, a stratified random sample of refine ries assigning strata based on, fo r example: size of the facility (our experience suggests that larg e facilities, even those that are well run, tend to have more fugitive emissions error and more error in general simply from having more sources); age of the

6 Lucas, Bob. (2007). Technical Memorandum to EP A Docket No. EPA-HQ-2003-0146 from Bob Lucas, EPA/SPPD dated August 20, 2007.

18facility (older facilities may not operate as well); complian ce record (facilities with more violations may have larger under-re porting error than other faciliti es). It appears there may be a more robust dataset (more benzene ambient air monitors located n ear petroleum refineries) that

could be assembled and evaluated in a more comprehensive manner.

If the analysis is limited by available monito ring data, the Panel reco mmends that rather than using a strict comparison of the model and monitoring results , the two data sets might be used in conjunction to provide a more comprehe nsive understanding of the probability or range of outcomes using, for example, a Bayesian appr oach. At a minimum, it would be useful to include a more formal uncertainty analysis and c onsider propagation of errors to better quantify the uncertainties and characterize the agreement with the benzene con centration data (See Bevington's book "Data Reducti on and Error Analysis" 7 ).

Finally, Appendix L attempts to put the potential error into context of the overall errors expected in the risk assessment, but may be misl eading in this regard. The statement on page L-1 regarding the analysis of the measured to modeled concentrations says,

“[The analysis] attempts to answer the que stion, “are benzene emi ssion estimates truly lower by a factor of 10 to 100 (at least for th ese 2 facilities), or are they close enough to

be useful in residual risk deci sion-making?” We attempt to an swer this last part keeping in mind the 2 orders of magnitude range of MIR values embodied in the residual risk

decision framework.”

This statement is not very clear, but could be interpreted to mean that the Agency might not view the level of uncertainty resulting from emissions estimate s as a large concern, given that the risk range for risk management decisions under the Clean Air Act spans two orders of magnitude. But such a view could be misleadi ng. Even if less than a factor of 10, an

underestimation bias in the emissions estimates should sti ll raise concerns, as it could prevent a source category from falling into the residual risk range that would othe rwise require remedial action. In contrast, as discussed below, questions such as whethe r the centroid of a census block is modeled or population migration is included ma y be on a level of detail and sophistication rendered obsolete given the inherent un certainty of the emissions input data.



### Appendix P: This appendix compares risk estim ates developed using RTR emissions inventory data with estimates developed usi ng emissions estimates from a process-based emissions model, the Refineries Emissions Mode l (REM). The results are informative. The comparison demonstrates differences in tota l emissions from refinery MACT 1 sources (Appendix P, Table 1) of a factor of almost 3 for benzene and a factor of 50 for methanol. However, xylenes and POM 72002 are in agreement to within about 50%. Th ere is a wide range

in the ratio of REM MIR value to RTR MIR valu e for individual facili ties (p. P-23), ranging from 0.1 to 5,000,000 (with all but one value rangi ng from 0.1 to 5,000). Also, estimated cancer incidence for the source category is 3-4 times hi gher using REM emission data relative to RTR emission estimates (Appendix P, Table 3). Instruc tive comparisons are also provided for specific

7 Bevington, Philip R. and D. K. Robinson , 2003. Data Reduction and Error Analys is for the Physical Sciences

, New York: McGraw-Hill Companies.

19emission sources such as fugitive equipment leak s, cooling towers, HAP storage vessels, and

areas for wastewater collection and treatment.

The assessment illustrates the problem the EPA encountered w ith the development of the emissions inventory for this source category. Th e analysis in this a ppendix actually almost

addressed the Panel’s concerns abou t the use of actual emissions as reported in the 2002 NEI. It

states that the modeled REM emissions are ba sed on MACT compliance or allowable emissions.

The difference shown in Table 3 between th e RTR-estimated “actual” HAP emissions (6,820 tons/year) and the REM allowable HAP emissi ons (17,800 tons/year) that are known to be emitted by MACT 1 petroleum refineries is stark. It is difficult to compare the risk results between these two emissions estimates and agre e with the conclusion that the REM database

results in a “modest increase in risk estimates” for the following reasons:

(1) the RTR used site specific emission point data (18 to 42% of the time) to estimate community impacts while the REM used default emission source release parameters for all HAP emissions placed them in the centroid of petroleum refining facilities and then

estimated the risk at the centroid of the cen sus block. This approach can underestimate the resultant MIR risk. The impact of consolidating emissions poi nts into a centroid emissions point for large facilities with multiple emissions points has been found to underestimate impacts in the area close to th e facility property boundary by a factor of 3

to 7 8 ; (2) the emissions estimates change the MIR cancer risk drivers (REM drivers are benzene, naphthalene and POM as compared to the RTR drivers naphthalene and POM); (3) the REM-based analysis excludes two more toxic groups of POM that would result in an increase of the MIR and cancer incidence; (4) the REM analysis results in increases in the cancer incidence and MIR ranking of the facilities even though the two more toxi c groups of POM are excluded; and (5) neither the RTR nor the REM emissions i nventories attempt to account for emission releases due to upset s and malfunctions.

The Panel does not agree with the closi ng statement of Appendix P, “Petroleum Refineries are highly regulate d facilities for which emissions are thought to be relatively well

understood (emphasis added) as compared to many other source categories. The relative similarity in MIRs may be unique in this case. It is difficult to genera lize the results of this analysis to other source categories”. This Panel is concerned this statement may convey a false degree of confidence in the emissions inventory that is not warranted for the source category as a whole, based on the information provided in the case studies.

Recommendations rela ted to Charge 1A : The comparisons provided in Appendices A, L, and P provide a transparent and useful look at the quality of the available emissions data for use in the RTR assessments. However, the results do not instill a high degree of confidence about the HAP emissions inventory, which is the foundatio n of the residual risk assessment. A poor emissions inventory will result in a poor re sidual risk assessment. The underestimation of

8 USEPA, 1998. Analysis Performed for the Risk Screening Environmental Indicators. Office of Pollution Prevention and Toxics. Available On-Line: http://www.epa.gov/oppt/rsei/pubs/index.html

20emissions will result in false negatives or underestimation of community risk while the overestimation of emissions and reporting of HAPs that are not expected to be emitted from the source category will result in false positiv es or overestimation of community risk.

It is readily apparent that the quality of the facility-specific HAP emissions inventories ranges from good to poor. Table 2-6 (p. 2-22) cl early illustrates this problem. There are 156 facilities in this data set and they do not cons istently report emissions that are expected for MACT 1 petroleum refinery processes. For ex ample, only 146 out of 156 facilities report benzene emissions, 129 facilities report xylene emissions, 136 facilities report toluene emissions, 130 facilities report hexane emissions and 104 report naphthalene emissions. There is no consistent reporting of polycyclic organic ma tter (POM) across faciliti es, although POM is one

of the identified RTR cancer risk drivers. Th ere are emissions of polyaromatic hydrocarbons (PAHs) total, POM, 16-PAH and individual PAHs by the facilities. It is unclear how any meaningful risk analysis could be undertaken for these emissions. There are five facilities that report a total of three tons of carbon tetrachloride emissions. The production and use of this HAP

has been banned under the 1990 Clean Air Act. Wh ile there are expected to be regional differences for some HAPs emitted from this source category (i.e. methanol and MTBE), some HAPs (e.g., benzene, xylene, toluene, and hexane ) should be reported by all facilities in the

source category. 9

The RTR case study models actual emi ssions using the 2002 National Emissions Inventory (NEI) and there apparent ly was an adjustment of thes e emissions using site-specific data from 22 refineries as provided by the Ameri can Petroleum Institute. However, it is not clear what adjustments were made. In particular, it is not clear whether all of the facilities’ emissions inventories were adjusted by using the inform ation contained in the August 6, 2007 technical memorandum on the Average Refinery Stream Composition. This technical memorandum clearly identifies the product sp ecific HAP emissions that should be expected from the sources subject to the MACT 1 Petrol eum Refineries NESHAP.

The primary goal of the residual risk assessm ent should be to assess the impacts of HAPs in the surrounding community with in the bounds of what is permissible or allowable under the

National Emission Standards for Hazardous Air Polluta nts. As a first step, the facility-specific

MACT 1 allowable emissions should be mode led. The modeling of NESHAP or MACT allowable emissions is necessary since the indivi dual facilities are allo wed by federal regulation to emit HAPs in these quantitie s into the surrounding communit y. The EPA cannot accurately

assess effectiveness of the NESH AP to reduce risk and be prot ective of public health and the environment by modeling actual emissions from these facilities, especially if the actual emissions are way below what is allowed to be emitted by the NESHAP. Beyond modeling residual risk from allowable emissions, a second step would be the modeling of actual facility

emissions to assess the current risk in the surrounding community. The RTR case study focuses on this second issue, but it does no t adequately address the issue that these facilities can increase HAP emissions to permissible NESHAP levels.

9 Lucas, Bob. (2007). Technical Memorandum to EP A Docket No. EPA-HQ-2003-0146 from Bob Lucas, EPA/SPPD dated August 6, 2007.

21The Panel recommends that EPA model RE M allowable emissions using the same emissions point information and toxicity factor s as used in the RTR to properly assess the residual risk associated with sources regul ated by the National Emission Standards for

Hazardous Air Pollutants from Petroleum Refine ries (Code of Federal Regulations Part 63 Subpart CC) (MACT1 Petroleum Refineries). This t ype of analysis will bette r assist EPA to meet with greater confidence the two-fold goal of the RTR as stated in the June 17, 2009 memorandum containing the char ge questions to the SAB.

The Panel recommends that EPA classify the emissions inventory (actual emissions) for the 156 facilities subject to this MACT standard by simple degr ees of confidence (high, medium or low). The categorization of the 156 facilitie s should consider size, throughput capacity and product refined. This evaluation should also incl ude statements about the confidence in the AP42 emission factors for the source category. The AP -42 manual already has a ranking system for

all of the individual chemical em ission factors. So a characterizat ion of the confidence in these values for the specific process emissions under eval uation should be included in the residual risk

assessment.

Finally, the Panel has some additional sugge stions for improving the HAP emissions inventory for these and other source categories su bject to residual risk assessments. First, EPA could adopt a consolidated emissions reporting rule for hazardous air pollut ants that requires all major facilities subject to Part 63 NESHAPs to uniformly report their actual and allowable emissions along with emission poin t parameters on an annual or se mi-annual basis. The two case studies presented in this review and previous residual risk asse ssments appear to have suffered because of the lack of a federal requirement to report HAP emissions in a consistent and uniform manner. An alternative way to address this issu e is to rely on facility specific compliance inspection information (state and federal) a nd Section 114 data requests. The information collected during compliance and enforcement pr oceedings is some of the most thorough information collected on facility specific emissi ons. Unfortunately, these data are usually sealed until an enforcement action is completed and in most cases will reflect sources that are out of compliance with state and federal air pollution sta ndards. The mining of these data is also labor

intensive. A third alternative would be to work closely with state and local air pollution control agencies to gather any facility specific emi ssions testing data that can be useful in the preparation of residual risk assessments.



### Charge Question 1B

As described in Section 3.2.1 and Appendix F, we developed mean and upper confidence limit

estimates for dioxins emitted fr om Portland cement facilities.

1B Does the approach used to estimate dioxin and furan emissions from Portland cement facilities represent the best av ailable methodology in support of a risk analysis? Can you suggest improvements?

22

### Panel Response

Because allowable limits were not modeled for D/F emissions from Portland cement facilities, we do not believe the approach used in the case study represents the best available methodology in support of a residual risk analysis. There is no need to estimate D/F emissions for Portland cement facilities, when allowable limits exist. The primary purpose of the risk and technology review (RTR) for Portland cement facili ties is two-fold: (1) to evaluate the residual risk to public health and the environment that remains after the application of the initial technology or emission limits contained in the Po rtland cement NESHAP; a nd (2) to critically analyze the performance of the air pollution cont rol requirements of the current NESHAP and evaluate whether the original allowable dioxin/furan (D/F) em ission limits could be reduced further, if this is shown to be technologically feasible by actual testing data. For the first step of this process, the Panel recommends that residual risk assessments be conducted using the current source-specific NESHAP allowable emission rate in combination with each facility’s maximum permitted production rate. This should be done whenever NESHAP emission limits have been set for specific hazardous air pollutants. In part icular, using estimated emissions that exceed the NESHAP limit is not appropriate fo r the residual risk assessment.

The final Portland Cement NESHAP, 40CFR Part 63 LLL contains two D/F emission limits: (i) 0.20 nanograms per dry standard cubic meter (8.7 × 10 –11 grains per dry standard cubic foot) (TEQ); or (ii) 0.40 nanograms pe r dry standard cubic meter (1.7 × 10 –10 grains per dry standard cubic foot) (TEQ) when the average of th e performance test run average temperatures at the inlet to the particulat e matter control device is 204 ° C (400 ° F) or less. For new and existing Portland cement kilns, the residual risk a ssessment should model these currently allowable

emission rates of D/F in combination with stac k flow rates corresponding to maximum permitted production rates for each facility. The information needed for this assessment should be available from the required compliance testing in formation for every Portland cement facility identified in the case study. If these allowable D/ F emission limits result in an unacceptable risk to public health and the environment after the completion of the multi-pathway risk assessment as conducted in the case study, a decision to lower these existing D/F limits should be made.

It appears that if EPA used allowable D/F emissions in its analysis, none of the Portland cement facilities considered would screen out of needing a refined multi-pathway assessment based on the emission thresholds presented in Appendix C-4.5.1. However, since the risk from D/F exposure is primarily driven by the fish and beef/dairy consumption exposure pathways, EPA could consider screening out facilities that have negligible potential to impact fishable waters and beef and dairy farms.

In the second step of the RTR process, th e NESHAP compliance testing information for D/F emissions from each facility should be collected and critically evaluate d to determine if it is technologically feasible to reduce the current Portland cement NESHAP D/F emission limits. This compliance information should be readily available upon request fr om the states or EPA regional offices. The information presented in th e case study demonstrates that the D/F emissions from the various kiln types can significantly va ry. The review of actual compliance data by kiln type could lead to the establishment of lower D/F emission li mits by kiln type sub-categorization

23as determined through a technology review of th e existing compliance data. The review should also address the issue that many Portland cemen t kilns burn alternative fuels that are not classified as hazardous waste (tir e-derived fuel, used oil) and th e influence of these materials on

dioxin emissions needs to be considered and noted in any future analyses. The availability of the D/F compliance testing data for th is source category should result in a more robust analysis of the technological feasibility of lowering these D/F limits by kiln type, which is independent of the residual risk assessment requirement.

A specific comment about how the risk assessm ent information for D/F is presented in Portland cement case study is warranted. The Agency should be cognizant of how the results of

the residual risk assessments will be perceived by the public in the impacted communities. Public concerns about the impacts of D/F emissions are extremely high. The methodology used in the case study could raise unnecessary public concern about fish consumption in the community, the consumption of beef and dairy produced in th e surrounding area, and adve rse effects on wildlife that would not be warranted if the Ravena plan t is in compliance with the current NESHAP D/F

emission limit. Based on additional information th at EPA provided to the SAB Panel, the use of the 95% UCL emission factor developed for wet k ilns and listed in Table F-3 would result in a

violation of the current NESHAP D/F emission limit. The application of this emission factor in

the residual risk assessment would result in a false positive risk result or an overestimate of the MIR risk. In general, residual risk assessments should rely on the use of NESHAP allowable emission rates when available for specific haza rdous air pollutants in combination with maximum production rates. In contrast, use of th e 95% UCL of available act ual data as a default emission rate estimate may be appropriate for i) source categories that do not have a NESHAP emission limit for D/F, and ii) al l other HAPs that do not have a current NESHAP emission limit.

Finally, EPA needs to carefully verify the emi ssion point parameters it uses in its analysis for the Portland cement industry. The stack exit te mperature they used in the case study for the Ravena facility appears to be off by 115 o F. The Agency’s draft RTR document lists it as 350 o F whereas the 2003 stack testing report fo r the facility indicates it is 465 o F.



### Charge Question 1C

As described in Section 3.2.2 and Appendix G, we estimated potential emissions of radionuclides, and associated inhalation cancer risk s, from two Portland ce ment facilities using very limited data and three diffe rent derivations. The results va ry by many orders of magnitude, but suggest that these risk s could be substantial.

1C Is this approach rigorous enough to cons ider placing it in the RT R assessment, which has regulatory implications? If not, given the lack of reliable emissi ons data for radionuclides, how can we improve the approach? If the quality of emissions data remains an irreducible stumbling block, can you suggest ways to obtain better emissions data?

24

### Panel Response

The Panel commends EPA for its effort to estimate emissions and cancer risks due to radionuclide emissions from Portland cement facilities. Emissions of isotope-specific

radionuclides warrant careful characterization and evaluation for Portland cement facilities and

other facilities that have the potential to emit relevant radionuclides. EPA’s proposed approaches to estimating inhalation cancer risks due to radionuclide emissions from Portland

cement facilities indicate that such risks could be substantial. EPA found mo re than 80 of the 91

facilities assessed had estimated Maximum Increm ental Risks (MIR) from radionuclide releases

in excess of 2 × 10 -6 (Exhibit G-12). However, the proposed analysis should not be formally included in the RTR assessment until further prog ress is made to quantify the isotope-specific

radionuclide emissions and the associated risks. The revised approach sh ould also consider the potential for multi-pathway exposure of isotope specific radionuclides (e.g., dietary exposure pathways, Exhibit G-13).

The Agency’s draft RTR document relies heavily on non-isotope specific radionuclide emissions reported in the 2002 National Emissi ons Inventory (NEI) for two Portland cement

facilities in California and on results from emissi on modeling for radionuclid es at the Maastricht Portland cement facility in the Netherlands (Leehouts et al., 1996, http://rivm.openrepository.com/rivm/bitstream/10029/10172/1/610053003.pdf ). EPA provided alternative evaluations with emissions estimated by scaling 210 Po and 222 Rn to clinker production; scaling to particulate matter (PM) emissions ; and by assuming all radionuclide emissions reported to the NEI were either 210 Po or 222 Rn. EPA clearly stated the assumptions used in estimating the radionuclide emissi ons under each approach. Howeve r, the assumptions need to be improved as described below before radionuclid e risk estimates are incorporated into RTR

assessments.

EPA’s analysis demonstrates that isotope-s pecific radionuclide emissions estimates are needed instead of using 2002 NEI data that do no t include such speciation. In particular, emissions and risk estimates EPA obtained by assuming NEI radionuclide mass emissions were

all 210 Po were implausible, as they were orders of magnitude greater th an estimates developed using process-based emissions factors. This result illustrates the importance of completing careful engineering review of input data before beginning risk modeling.

Radionuclides such as uranium and thorium also exist in many geological materials at ppm(m) concentrations. The radi onuclide content of f eedstocks used to produce Portland cement should be characterized at important locations acr oss the US where these feedstocks are mined. Other toxic trace elements, such as mercury, could also be considered at the same time. Such

information should be available in the literature, as it is for other geologic materials such as

fossil fuels. EPA’s Indoor Envir onments Division (IED, located within ORIA and under OAR), the US Geological Survey (e.g., Radioactive Elemen ts in Coal and Fly Ash: Abundance, Forms, and Environmental Significance, USGS Fact Sh eet FS-163-97, Oct 1997), the National Institute of Standards and Testing (NIST), the US Nuclear Regulatory Co mmission, and nuclear engineering and geology departments at academ ic institutions are possible sources of such information. Any source category that has the po tential to cause increased local exposure to airborne radon and polonium needs to have this issue addressed as part of the RTR process.

25With information on radionuclid e content of feedstocks, sc reening material balance

calculations such as those done by Leenhouts et al. (1996) for the Maastricht facility should be performed to estimate isotope-specific radionuclid e emissions from Portland cement facilities. This analysis should use data for US feedstoc ks and estimate the atmospheric emissions that would occur after implementing MACT. Thus , a much improved screening for potential radionuclide emissions should be performed by using mean and upper conf idence limit literature data for isotopes in the feed materials and in formation about the operating conditions of the facility (e.g., temperature and chemical reactions in the proces s). Such information may also provide insight as to how to reduce radionuc lide emissions during the production of Portland

cement.

If results from revised screening calculations are not acceptable or data are not available to support such analyses, then source informa tion describing isotope-specific radioactivity should be obtained from select Portland cement facilities, including results from stack tests. Such information should include descriptions of the isotope-specific radionuclides that are processed

and then emitted from the Portland cement facilities.

Emission characteriza tion of the radionuclides could also be improved by evaluating closure between measured radioactivity at r eceptors near a Portland cement facility to radioactivity predicted using estimated source strengths and dispersion modeling; this evaluation would be similar to what was done for the petr oleum refinery case study in this review. The feasibility of undertaking such an evaluation assu mes ambient radioactivity levels are detectable near the sources, considering ba ckground values and detection limits of analytical techniques.



### Charge Question 2

Section 2.2.2 describes our inputs to the AE RMOD dispersion model for RTR assessments. We have performed the following analyses in an attempt to better understand the uncertainties and/or potential bias th at may be introduced by some of these inputs:

• Section 4.4 compares exposure estimates based on one and five years of meteorological

data.

• Section 4.5 presents an analysis of how the location of the meteorological station used for modeling affects the outcome.

• Section 4.6 presents an analysis of the eff ect on risk estimates of omitting atmospheric chemistry from the modeling of a high-impact refinery.

• Section 4.7 presents an analysis of the e ffect on risk estimates of omitting deposition from the modeling of Portland cement facilities.

26• Section 4.8 and Appendix M presen t a sensitivity analysis of the uncertainties arising in the refineries assessment by estimating exposures at census block centroids rather than at

the nearest residence.

2 Do these analyses adequately support the practices of (1) using a single ye ar of meteorological data, (2) using facility-supplied meteorological data, when available, (3) omitting atmospheric chemistry from modeling, (4) omitting deposition from modeling, and (5) using block centroids as surrogate exposure locations for these case studies? If so, can any or all of the analyses be applied to other source cate gories? If not, can you suggest ways we might improve them?



### Panel Response

The dispersion modeling for primary HAPs used in risk assessments is well developed and appropriate. Any modeling entails uncertainties, and the series of case studies presented in Section 4 provide a broad picture of model perfor mance and sensitivity for this risk assessment. EPA has presented calculations justifying the us e of several simplifica tions (i.e., assumptions) for performing longer-term impact and risk asse ssments. Some simplifications were shown to introduce relatively minor changes to risk estimates most of the time. However, there were some areas where simplifications introduced changes in ri sk estimates that could be appreciable, and in other areas further investigation is required in order to adequate ly justify the conclusions. The following discussion highlights some of the impact s of these assumptions on the risk assessment.



### Use of a single year of meteorology : The sensitivity analysis of the use of one versus five years of meteorological observations is well done, and shows that most of the time,

uncertainties of less than 10% are introduced in calculated concentrations, although maximum annual or hourly concentrations can differ by up to 10-40% at some locations and times. While the conclusion of this section sugg ests that uncertainties in risk estimates due to the inclusion of more meteorological observations are minor if reported risk estimates are limited to one significant figure, we suggest that use of more than one year of meteorological observations is desirable in order to capture wo rst-case scenarios. At most s ites, numerous years of meteorology

observations are available and should be examin ed to ensure impacts are not underestimated.

If more meteorological observations are us ed in any longer-term impact analysis, markedly higher concentrations and impacts may be encountered on hourly scales, while annual averages are expected to fluctuate by smaller amounts (relative to maximum hourly impacts) under the influence of more smoothly-varying aver aged year-to-year meteorological variations. It is standard EPA procedure in New Source Review permitting to utilize five years of meteorological data, and the SAB recommends fo llowing this protocol when feasible. Unless there are serious computational or labor resource limitations, we suggest that maximum annualaverage impacts be defined from the worst year of several years’ analysis. Acute impacts should be calculated using the worst 1-hr impacts calculated using wh atever number of years of meteorological data is available for analysis.

It appears that there is a potentially serious underestima tion bias in the dispersion modeling due to the ambiguous treatment of “calm” periods that have no definable wind

27directions. This factor could be contributing to AERMOD calculating lower concentrations than observed, as seen in the petroleum refine ries case study (Appendi x L). The highest concentrations generally occur during calm periods , and the emissions modeling analysis appears to ignore calm periods, treating them as equivale nt to missing meteorological measurements. By ignoring these periods, potentiall y significant errors that underestimate maximum concentrations will result. Such a simplification needs to be investigated before concluding that emissions information might be biased low. EPA should cl arify how calm periods ar e treated in AERMOD, and consider whether the approach needs to be revised to avoid underestim ating risks and health impacts.

The methods for choosing an individual year for risk assessment suggested in the Agency’s document could be applied to other source categories, but depending on source stack characteristics, some of the quantitative conc lusions of EPA’s sensitivity studies may not transfer. Apparently for the refi nery source category used in th is 1 versus 5 year sensitivity study, HAP emissions were mostly ground-level s ources without significant stack heights or plume rise. For other source categories that ar e emitted in buoyant plumes or from elevated stacks, the confounding effects of plume rise will appreciably influence calculated impacts, and it is possible that differences between 1 and 5 years of meteorology could be greater than the differences shown in this sensitivity study, which was dominated by ground-level sources.



### Use of facility-supplied meteorology 10 

### : The choice of meteorological data for performing risk assessments appears to have a significant impact on calculated risks, as demonstrated in the sensitivity studies presented in section 4.5. In this section, EPA compared risk estimates for four petroleum refineries th at were derived using meteorological data from

three to five different meteor ological stations, each within about 200 km of the source. The “overall summary” of this section that “differences usually fall within rounding error for the onesignificant-figure characterization of risk” is some what inconsistent with the results shown in Table 4-2, which show that differe nces greater than a factor of two are common, and there is no

consistent trend in these differe nces with distance from emissi on source. In all likelihood, these appreciable differences result from the fact th at even the closest National Weather Service (NWS) meteorological monitoring station only cr udely captures the hourly meteorology that is representative of conditions near emission s ources and impact receptors. Over broad areas, especially in the western U.S., there can be gr oss errors introduced in air quality impacts calculated using the closest NWS meteorologi cal monitoring stati on. Sometimes several mountain ridges or valleys may lie between a partic ular site and a meteorological monitor. Given

the small horizontal scales of 1-hr winds (boundary-layer scale – less than 1-2 km), one would expect discrepancies similar to those shown in this sensitivity study for monitors separated by only 1-10 km from some source locations. As noted in comments on Appendix L, it would be desirable to use facility-provide d meteorology for risk assessments , if available. Unfortunately, site-specific meteorology is proba bly not available for most f acilities, and this remains a significant source of uncer tainty in any risk assessment calculation. The potential errors

10 It appears that this charge question is poorly worded, since the use of “facility provided” meteorology is not addressed in the sensitivity study. In the preamble to the charge question it is noted that the study covers the “location of meteorological station”, and section 4.5 mentions that two refineries furnished meteorology data, but results from these “facility-supplied” meteorology are not presented. Therefore, the Pa nel interpreted the charge question to more generally consider EPA’s selection of meteorological stations.

28introduced by using meteorology that is not repres entative of a given source or receptor location is partially ameliorated by using as long a record of meteorological data as is computationally feasible, to increase the probability that high impact conditions are encountered and included.

The underlying problem of using meteorology that is representative of each source location is an endemic problem for any risk assessment irresp ective of the source category considered. The best method for quantifying whether the closest NW S station meteorology is “representative” of any emission source point would be to quant itatively compare source-specific onsite meteorology measurements with nearby NWS m onitors, and perform sensitivity analysis comparing the use of onsite meteorology versus using the nearest NWS observations as input. This direct comparison was not done in this anal ysis, as onsite data were not included in the comparisons, so the conclusions of this sec tion suggesting that usi ng “nearest NWS” site meteorology introduces relatively minor uncertainties in risk assessments is not well established by the sensitivity studies presented here.



### Omitting atmospheric chemistry : Many emitted HAPs undergo relatively slow photochemical oxidation following release. The sensitivity study presented in section 4.6 addresses only the decrease in concentrations of emitted (primary) HAPs due to oxidation during photochemical aging. It is well known that the tim e scales for photochemical transformations of most HAPs are considerably longer than the transport times between sources and highly impacted receptors, and therefore the concentra tions of emitted HAPs will decrease by relatively small amounts due to photochemical processes. Under these conditions , ignoring atmospheric chemistry would be reasonable fo r these risk assessments, and th e sensitivity study presented in section 4.6 adequately demonstrates this.

However, several organic HAPs (e.g., formaldehyde) are formed dur ing the oxidation of other emitted volatile organic compounds, and it is not obvious that ignoring photochemical formation of secondary (formed) HAPs is re asonable. Therefore, an additional study of secondary HAP formation needs to be performed in order to rule out the need for incorporating complex photochemistry in these risk assessments. Such a sensitivity study could involve running a short-term (2-4 hour simulation) phot ochemical “box model” including a gas-phase chemical mechanism under typical daytime condi tions for a broad range of VOC/NOx emission profiles representative of various source categories, then estimating the secondary formation of HAPs such as formaldehyde. The calculated con centrations of secondary HAPs from a simple box model alone could provide conc entrations that could then be used as inputs to screening models of potential risk assessments to ascert ain whether secondary HAP formation could be an important contributor to ai r quality risk endpoints.

The results of EPA’s analys is of the omission of HAP decay in risk assessments could be applied to other source categories. Howe ver, it is possible that secondary HAP formation could be significant for some HAP source categories. As noted above, further sensitivity studies of secondary HAP formation would be required to rule out the necessity of including complex photochemical modeling for future HAP risk assessments.



### Omitting deposition : It is well known that the time scales for deposition are considerably longer than the transport times between sources and highly impacted receptor

29locations, so during this time the concentrations of emitted HAPs will decrease by relatively small amounts due to deposition. Under these conditions, ignoring deposition would be

reasonable for these risk assessments. Section 4.7 confirms this conclusion through a rigorous and reasonable comparison of risk assessments performed with and without deposition, showing changes of a few percent or less for a handful of facilities. Based on this study, it is expected that the simplification of omitting deposition could be generalized to other source categories when performing HAP risk assessments.



### Use of census block centroids rather than the nearest residence : This analysis suggests that cancer risks calculated at census block centroids are usually the same, or sometimes considerably greater than (up to 2000%!) risks calculated at individual residences within a census block. This analysis appears to contain some fundamental simplifications that render the results somewhat ambiguous. It appears that risk impacts have been interpolated to residence locations from centroid and polar grid r eceptors, rather than explicitly calculated using AERMOD (Appendix M). Furthermore, the residen ce impacts have been unrealistically set to centroid impacts if the census blocks are “small”, or if residences are “n ear” the centroid, or if the polar grid was “not adequate” to interpol ate to a particular residence. These vague interpolation methods will produce residence impacts that are iden tical to the centroid impacts quite often in an unrealistic fashi on. It is also possible that the conclusions of this sensitivity study may be an artifact of the pa rticular configurations of ce nsus block maps and residence

locations used for the subset of facilities (21 of 154) chose n. It is possible that large

underestimations of risk could o ccur for other facilitie s, other source categories, or census block/residence configurations.

In order to determine whether impacts at cen sus block centroids reas onably assess risks at

actual residences within censu s blocks, the HEM-AERMOD system should be run twice with different sets of receptors: (1) a receptor grid of census block cen troids, and (2) a receptor grid with residences tagged as receptors. Maximum h ealth risk impacts would be directly compared using these two receptor grids for a number of facilities. The AERMOD model itself should be

run for actual residences in order to accu rately assess risks at those residences.

Another area of concern related to this sens itivity study entails the use of a limited subset (21 of 154) of facilities consider ed. In order to compare centroi d versus residence impacts and draw general conclusions, it is not necessary to explicitly simulate all 154 facilities associated with this source category; a carefully chosen, strati fied subset of facilities could be used to draw more general conclusions. In this study, the subset was restricted to the 21 facilities with the greatest MIR. These 21 facilities may not be re presentative of the rang e of possible censusblock/ residence locations, meteorology, and source configurations that would influence the differences between impacts at residences and cent roids. Clearly a better criterion must be used to define a “representative” subset of test cases . For example: urban, subu rban and rural facility locations should probably be sampled, even if some of these facilities have low impacts.

It is possible that the conclusions of any se nsitivity study of recepto r locations will not be generally applicable to other source categories. HAP emissions for this sensitivity study are dominated by ground-level sources without significant stack height s or plume rise. Under these conditions the greatest impacts will be in census bl ocks closest to the facilities. For other source

30categories that are emitted in buoyant elevated st acks, the confounding effects of plume rise can move the regions of greatest impact further fr om the source locations. It is possible that

differences between block centroi ds and individual residences could be greater than the differences shown in this sensitivity study fo r source categories that are characterized by elevated buoyant emissions from smokestacks.



### Charge Question 3A

Section 2.2.6 of the Agency’s draft RTR docum ent describes our proc ess of selecting and prioritizing dose-response values for RTR human health risk a ssessments. We select chronic dose-response values in the same way that we do for NATA, a process that the SAB has already

reviewed in the context of NA TA but not one of regulatory d ecision-making. We have also developed an analysis (presented in Appendix O) of the possible importance of HAPs that lack chronic dose-response values. This analysis suggests that only a few HAPs lacking such values could be important, with the degree of importance heavily dependent on the conservatism of the input assumptions.

3A Is our process of selecting and prioritizi ng chronic dose-response valu es appropriate for RTR

risk assessments? Should we cons ider additional sources , or a different prio ritization process? Can the analysis of unassessed HAPs be impr oved by developing prior assumptions regarding the toxicity of these HAPs, and if so, how shoul d this be done? Are there other ways we can improve it? Is this approach inherently limited to the curr ent bounding exercise and tool for identifying research needs, or can it be further developed and incorporated into RTR assessments? Can you provide advice on how we can incorporate HAPs lacking dose-response

values into our risk characterizations?



### Panel Response

Process of selecting and prioriti zing chronic dose-response values : The approach used in the RTR assessments is reasona ble, but too simplistic in that it accepts doseresponse numbers at face value, without much u nderstanding of the qual ity or validity of the value(s) chosen. Of concern is that some values have been de veloped quite some time ago using older data, which may be obsol ete, while others have been developed more recently and incorporate new findings. Even dose-response values that use the same up-to-date database are not equivalent, as different agen cies do not derive hazard values in the same way. For example, for the benchmark methods, EPA and CalEPA apparently both take the lower 95 th confidence limit of the dose of interest, but then look at the dose level that causes a 10% (EPA) or a 1 or 5% (CalEPA) incidence of the critical effect. In many cases, the differences in chronic doseresponse values will not significantly alter the RT R risk assessment, but they do suggest a need to carefully consider any signi ficant differences in chronic do se-response values so that the credibility of the risk assessment is not impair ed by selection of an out dated data point.

To address this concern, the Panel recommends th at a table of chronic toxicity values be

created. The table should include all the chemicals under consider ation, all of th e eligible dose31response values (e.g., if both EPA and CalEPA ha ve values for the same chemical, both should be included), the source of the value, the year the value was last updated, and a qualitative description of the effect (e .g., eye irritant, neurotoxicant, reproductive toxicant, cancer classification) as all effects do not have equal hea lth impacts. The entrie s in the table should be reviewed for consistencies among the values ava ilable for each chemical. If the chronic doseresponse values are significantly different between ag encies, especially if th e value is a driver for the risk assessment, a review should be conduc ted to understand why the values differ. By necessity, professional judgment will need to be used during the chronic dose-response value selection process to decide which value is mo st appropriate to use based upon thoroughness of the data review, consistency of the dose-res ponse modeling with the und erlying science base, and the Agency’s objectives for public health protect ion. All of this analysis can be part of an appendix, with the text only having the inform ation selected for use in the assessment.

Furthermore, if a chemical appears to be a driver of the assessme nt, the assessor should further review the value and examine how recently it had been developed. If it was developed more than several years ago, a li terature search should be perfor med to identify studies that may alter or update the value. If su ch studies are identified, the chemical should be considered for recommendation to the Integrated Risk Assessmen t System (IRIS) high priority revision list for review of the dose-response value.

The preferred database for chronic dose-respons e data should be the IRIS database. The Panel strongly recommends that EPA update th e values in IRIS and provide the resources necessary to maintain th e updating process. Concern about the quality of the IRIS database and approaches to keeping it up-to-date have prev iously been addressed by the SAB and others. 11

The Panel endorses these recommendations for ch ange in the IRIS database and process for updating the database.

The use of additional sources of data should be considered; however, if additional sources of data are used they should be ones that have undergone adequate and ri gorous scientific peer

review. The inclusion of additional sources of dose-response values into the hierarchy needs to be adequately documented in a transparent manne r in any residual risk assessment case study.

The American Conference of Governmental Industrial Hygienists Threshold Limit Values (ACGIH TLVs®) could be considered fo r use as an additional source of data for screening purposes, when other va lues are not available. The TLVs have been determined for healthy workers; therefore, for use in the residua l risk process, the values would require time adjustment from a 40-hr workweek to a 24 hr /day, 7 day/ week exposure (168 hrs/week). Further adjustment for consideration of protect ion for susceptible populations would be needed,

and if a TLV is not considered a No Obse rved Adverse Effect Level (NOAEL), another adjustment factor might be needed.

11 As stated on page 56 of the Residual Risk Report to Congress under the heading, Data Availability, Limitations, and Closing Data Gaps, the preferred source of dose-response data for conducting federal risk assessments is the IRIS database. However as discussed in a recent GAO report (available at http://www.gao.gov/new .items/d09773t.pdf ), the IRIS database is at serious risk of becoming obsolete due to an absence of timely updates of existing IRIS values and a significant backlog of ongoing assessments.

32

### Analysis of unassessed HAPs: The SAB has previously commented on the importance of having reliable dose-response values for all of the HAPs listed in the 1990 Clean

Air Act. 12 The residual risk exercise emphasizes, once again, the importance of having accurate, current information in the Agency's IRIS database.

Appendix O provides the rationale for estima ting dose-response values for HAPs lacking these values, based on chemicals that have al ready been thoroughly evaluated. The appendix presents an interesting attempt to fill the void an d create some type of toxicity ranking scheme to prioritize HAPs for toxicity testing and dose-response assessment and for the use of surrogate reference concentrations (RfC) and unit risk estimate (URE) values in the residual risk assessment process. However, there appears to be limited and highly variable information about the emissions of some of these HAPs, which handi caps the prioritization pr ocess. Furthermore, the HAPs that are being reviewed by this process have large da ta gaps for which professional judgment is needed to derive surrogate RfCs and UREs. This approach creates more uncertainty in the selection of a surrogate RfC or URE fo r use in the residual risk case studies.

We assume based on our reading of the case st udy text that surrogates were chosen as

follows: All values in Table 1 of the indicated re ference were evaluated for percentiles, resulting in the table at the top of page O-2. Thus, a ch emical having no URE or RfC is assumed to fall into the same percentiles as chemicals that had such values. Then the emissions of a chemical having no URE or RfC were multiplied by the pe rcentiles, creating values that show up on Figure O-1. The Panel recommends that the Agency expand the methods discussion to include a better description of the toxic ity weighted emissions (TWEs) for chemicals having UREs and RfCs, using some of the language from the Air Toxics Risk Assessment Reference Library (see http://www.epa.gov/ttn/fera/dat a/risk/vol_3/Appendix_B_April_2006.pdf or http://www.epa.gov/ttn/fera/d ata/risk/vol_1/chapter_06.pdf ).

In addition, the discussion of how surrogate s were chosen should be made clearer.

Limitations about the emissions da ta need to be identified and addressed. For example, only one facility out of 104 Portland cement facilities repo rts 48 tons per year of carbonyl sulfide. This questionable emissions data drives the TWE pro cess in Appendix O and carbonyl sulfide is listed as a priority HAP for further dose response eval uations. If our assumptions above about the calculations of surrogates are co rrect, and if a verification of the emissions inventory is conducted, the approach is adequa te for screening purposes.

Any unassessed HAPs that screen-in because of this evaluation process should then be followed-up by reviewing existing toxicity informa tion to examine the likelihood that they could be a driver for the assessment process.

The current bounding exercise and tool for identi fying research needs is limited to this purpose and probably cannot be further develope d and incorporated in the RTR assessments given the limitations of the emissions inventory for these HAPs

. HAP-specific emissions testing

12 Review of the US EPA’s report to Congress on Residual Risk. EPA-SAB-EC-98-013; Advisory on the USEPA’s draft Case Study Analysis of the Residual Risk of Secondary Lead Smelters. EPA-SAB-EC-ADV-00-005; Advisory

from the National-scale Air Toxics Assessment. NATA – Evaluating the National-Scale Air Toxics Assessment 1996 Data – SAB Advisory. EPA-SAB-EC-ADV-02-001.

33would have to be conducted at these facilities in order to use and have confidence in weighting

factors that are based on the am ount of actual HAPs released.



### Incorporation of HAPs lacking dose-response values: The Panel recommends that the Agency prepare or compile toxicity profiles for each of the HAPs that Appendix O identifies as having th e potential to drive the RTR asse ssment. They should receive a very high priority for evaluation according to the IRIS process that was recently redefined by Administrator Lisa Jackson. (See http://www.gao.gov/new.items/d09773t.pdf for a review of recommendations and changes to be made to the IR IS process). Residual risk decisions for these

chemicals will have to be identified as awaiti ng peer review or Agency-wide consensus.



### Additional issues regarding chronic dose-response values: The Panel was not charged with critiquing the IRIS methodology itself and theref ore was not constituted with the expertise for an in-depth review of the methodology. However, we note below that

inhalation risk methods for children are st ill developing and that California Office of Environmental Health Hazard Assessment (OEH HA) has very recently updated its methodology in ways that could affect th e development of RfC and URE values. US EPA should examine these developments to make sure that the RT R process adequately covers children's residual

risks.

In particular is the question of whether th e interindividual vari ability factor for noncarcinogens and the standard cancer unit risk de rivation adequately cove rs children. If it does not, it is a potentially significant uncertainty gi ven the greater intake rate of children via inhalation and sensitivity to carcinogens and other toxicants. 13

California EPA/OEHHA has determined that inhalation dosimetry for children is sufficiently different from adu lts to warrant a full 10-fold intra-individual pharmacokinetic uncertainty factor (i.e., an extra 3-fold PK uncertainty for childr en relative to the IRIS method) as a default approach. In setting non-can cer reference exposure levels (RELs), Cal EPA/OEHHA also considers that children may be outliers in terms of chem ical susceptibility and on a case-specific basis adds a children's pharmacodynamic factor of 3-fold, making the

inhalation risk for children as much as 10 times greater than adults). 14

This issue of childrens’s hazard should be pres ented as an uncertainty with regard to noncancer dose-response assessment and carcinogen dose-response assessment, especially considering that only two mutagenic carcinogens receive the age-adju sted potency factor approach in the RTR, even though numerous ot her mutagens (e.g., 1,3-butadiene) are analyzed. California’s OEHHA uses the children's cancer pot ency adjustment factors on a much broader array of carcinogens th an the narrow interpretation used in the Agency’s draft RTR document. 15

13 USEPA, 2005. Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens EPA/630/R-03/003; Barton HA, Cogliano J,Flowers L, Valcovic L, Setzer RW, Woodruff TJ. 2005. Assessing Susceptibility from Early-Life Exposure to Carcinogens . Environ Health Perspec tives 113:1125-1133; Hattis D,Goble R,Russ A,Chu M, Ericson J. 2004. Age-relate d differences in susceptibility to carcinogenesis: a quantitative analysis of empirical animal bioassay da ta. Environ Health Perspectives 112:1152-1158.

14 ( http://www.oehha.ca.gov/air/hot_spots/2008/NoncancerTSD_final.pdf

15 http://www.oehha.ca.gov/air/hot_spots/tsd052909.html

34This would be a natural area for sensitivity analysis (e.g., applying the age-adjusted potency factor to numerous carcinogens (a t least all those that are mutagens) to determine the degree of uncertainty children's vulnerability can cr eate in the cancer risk assessment.



### Charge Question 3B

We developed our selection process for acute dose-response values more recently than the one for chronic values, and it has not yet undergone SAB review. The universe of acute health benchmarks contains many gaps, as shown in Table 2-5. In addition, some of the benchmarks correspond to “no-effect” levels ( e.g. , CalEPA acute reference exposure levels, which are analogous to chronic RfCs), while ot hers correspond to “mild-effect” or “severeeffect” levels ( e.g. , acute exposure guideline levels) that are intended to guide authorities in making emergency evacuation decisions. For these reasons we have not applied a prioritization

scheme.

We have not generally incl uded acute minimum risk leve ls (MRLs, developed by the Agency for Toxic Substances and Disease Regist ry, or ATSDR) as dose-response values in our assessments of acute risks because of a temporal mismatch between the exposure estimates (based on one hour) and the MRLs (b ased on 24 hours to two weeks).

3B Given these gaps and inconsistencies among av ailable acute benchmarks, do the case studies characterize acute risks adequate ly? Should we include ATSDR MRLs in our assessments, and if so, how can we solve the temporal mismatch ? Is the use of emergency guidelines in our assessments adequately described and interpreted? Are there other acute health metrics EPA should consider using for these assessments? Do you have suggestions for improvements in any

of these areas?



### Panel Response

Adequacy of the case studies in characterizing acute risks: The case studies characterize acute risk adequately, but this may be due to the unique circ umstances of these two case studies. Thus, there is a need to pay atte ntion to the principles and practices used. The incorporation of the available California Reference Exposure Leve ls (RELs) for the assessment of acute effects is a conservative and acceptabl e approach to characterize acute risks.

The Panel has some concern with the use of the Acute Exposure Guidelines Limits (AEGLs) and Emergency Response Planning Guidelines (ERPGs). These limits were developed for accidental release emergency planning and are not appropriate for residual risk assessments without modification because, as described in the AEGL and ERPG documentation, adverse effects may occur at these levels. For example, at the AEGL-1 level, “...the general population, including susceptible individuals , could experience notable disc omfort, irritation, or certain asymptomatic nonsensory effects. However, the effects are not disabling and are transient and reversible upon cessation of exposure.” (citation-in each AEGL document). Some of the AEGLs and ERPGs listed in Table 2-5 are higher than valu es used to protect heal thy workers from acute

35effects in occupational settings. The Pane l recommends considering reducing the AEGL1/EPRG-1 emergency guideline values by a fact or of 3, when the value is based on a LOAEL rather than a NOAEL. This would better approxim ate a “no-effect” level, as in RfC’s. In contrast, AEGL-2 and ERPG-2 values should never be used in residual risk assessments because they were derived on the basis of maximum con centrations that would result in serious or

irreversible health effects if they were exceeded.

The short-term exposure levels (STELs) a nd ceiling levels used by the American

Conference of Governmental Indus trial Hygienists (ACGIH), O ccupational Safety and Health Administration (OSHA) and the Na tional Institute of Occupationa l Safety and Health (NIOSH) were developed to protect healthy workers from short exposures that may routinely occur in the workplace. The use of acute dose response values that are greater than occupational values used to protect healthy workers does not provide a hi gh degree of confidence that the dose response values used in the case studies have adequately characterized the acute risk of HAP exposures for sensitive subpopulations within a community. For example, the use of the AEGL-1 for 1,3- butadiene (1500 mg/m 3 ) versus the OSHA short-term exposure limit (11 mg/m 3 ) calls into question the adequacy of the use of emergency pl anning values in any residual risk assessment.

The Panel does not recommend the use of the ATSDR MRLs in the risk assessments as their use would require a potentially complex corr ection for the temporal mismatch. In order to use the MRL values, the risk assessors would ha ve to recalculate an acute value based on the critical endpoint(s) identified in the ATSDR documentation. A ppropriate safety factors would have to be determined and applied to the cr itical endpoint to determine an acceptable acute exposure value. Without peer review of the cal culated value, the credib ility of the assessment would be questionable even in a screening assessment.

When other more reliable values are not available, it is recommended that adjusted occupational values (ACGIH TLV) be considered for use in the risk assessments. The acute TLV values represent an evaluation of the liter ature that, by using expe rt judgment, could be adjusted and considered for use. Because subs tantial risk may remain with exposure at TLV levels, these values should only be us ed after thorough and critical evaluation. 16

Other sources of peer-reviewed health va lues are the Spacecraft Maximum Allowable Concentrations for Selected Airborne Contaminants (SMACS). 17 SMACS are defined as “the

maximum concentrations of airborne substances that will not produce adverse health effects, cause significant discomfort, or degrade crew performance” and are classified into 1- and 24- hour emergency SMACS and 7-, 30-, and 180-d c ontinuous SMACS. SMACS are developed in

16 Roach SA, Rappaport SM. But they are not thresholds: a critical analysis of the documentation of Threshold Limit Values. Am J Ind Med. 1990;17(6):727-53; Robinson JC, Paxman DG. The role of threshold limit values in U.S. air pollution policy. Am J Ind Med. 1992;21(3):383-96; Castleman BI. Legacy of corporate influence on threshold limit values and European response. Re: Am J Ind Med 44: 204-213, 2003. Am J Ind Med. 2006 Apr;49(4):307-9.

17 Spacecraft Maximum Allowable Concentrations for Se lected Airborne Contamin ants: Volume 5 (2008) Committee on Spacecraft Exposure Gu idelines, Committee on Toxicology, National Research Council ( available at http://www.nap.edu/catalog.php?record_id=12529 ). All five volumes are available at www.nap.edu

.

36a similar way to other health va lues, except that they typicall y do not include an uncertainty factor for susceptible subpopulations because the ta rget population is a healthy adult population. Furthermore, many of the SMACS represent effect levels, rather than “safe” levels, so they

would need to be dealt with in a manner similar to emergency values. It is recommended that

EPA add these documents to its list of sources for analysis. Because susceptibilities are not accounted for, these values would need to be divi ded by an uncertainty factor of 3 or 10 (similar to the adjustment recommended for AEGL-1 or ot her acute values), and then compared to other values. Also, if the SMAC was related to a LOA EL, another uncertainty f actor (3 or 10) would be needed to adjust to a NOAEL.

As per the recommendations for the chronic table, a table of acute values should be developed, with the following columns created for each table: CAS, AEGL-1, etc. (as in the top row now, as modified based on the recommendations above). For each value, the year the value was last updated should be incl uded and a qualitative de scription of the effect should be provided (e.g., describe the critical effect used as the ba sis of the calculation). Ne xt, the table should be examined for consistencies. For example, if the values from different agencies are different, the reasons should be explored. Perhaps one value is more recent than another; perhaps the critical effect is different. Such a table is complex a nd therefore a candidate for an appendix, with the summary result being in the main text.

Minor recommendations for clarification:

a. p. 2-13 bottom. The text should be revised to identify that the acute REL is for 1 hour. They also have 8 hour values, but we presum e the analysis used the 1-hour values to make them equivalent to others.

b. p. 2-14 top description of AEGLs. In the middle of the paragraph, it says that the values range from 10 minutes to 8 hours. This is true, but they have explicit values for 10 minutes, 30 minutes, 1 hour, 4 hours, and 8 hours. Thus, the text should be expanded to indicate this. We presume the analysis used the 1 hour value for consistency.

The following minor edits are recommende d. On page 2-16 Table 2-5: (a) The table title should be revise d to say 1-hour acute exposure. (b) The table should be footnoted to define the AEGL-1, etc. (The definition is in the text, but tables should stand alone.)



### Charge Question 4A

Section 2.2.3 describes the process by wh ich we estimate chronic human inhalation exposures based on modeled average ambient con centrations at census block centroids. For these case studies, this process did not include c onsideration of either da ily behavior pattern or long-term migration behavior. Section 2.2.3 presents a rationale for omitting daily behavior, and Appendix N presents a case study that adjusts inhalati on-based lifetime cancer risk estimates for

individuals to account fo r long-term migration.

374A Does our process of estimating inhalati on exposures adequately support regulatory rulemaking? Is our rationale for omitting daily behavior convincing, or does the omission compromise the value of our assessments? Should this, or some other, adjustment for long-term migration be incorporated in to our risk assessments?



### Panel Response

An overarching concern with EPA’s chroni c inhalation exposure estimates is that children’s exposures do not appear to have been adequately addressed. The differences in exposure between children and adults should be carefully considered and discussed in the exposure assessment. Otherwise, EPA’s overa ll approach appears to be a reasonable screening approach for localized impacts (e.g. neglecti ng processes like deposition, plume depletion, atmospheric degradation) that can be refine d further. In addition, EPA identifies some assumptions that could potentially lead to dow nward bias, such as not considering population growth or future expansion of production. Alt hough these assumptions ma y be appropriate given the need to simplify the analysis, periodic re assessment may be needed, especially in

circumstances where there are substantial cha nges in population growth and production levels.

With regard to the chronic inhalation exposure estimates, the Panel finds the rationale for omitting daily behavior to be convincing. Given the age of some of the av ailable activity pattern data and the inherent community-scale activity pattern uncertainties between locations, the decision to omit daily behavior is justified. The Agency's draft RTR document should make it clear that consideration was gi ven to daily behavior in term s of time spent indoors and past experience has shown it makes little difference in risk estimates.

The Panel further recommends that long-term mi gration not be incorporated into the risk

assessment. It does not add value to the risk assessment and introduces additional uncertainty. As discussed in Appendix N, the migration data th at would be used to modify the risk estimates have not been scientifically peer-revie wed and are limited in their geographical representativeness. While this preliminary anal ysis does not merit being part of the central assessment, it is worth leaving in the appendix and referencing in the text. The Panel encourages EPA to continue to improve understanding of activ ity patterns and migration behavior to support

future refinements.



### Charge Question 4B

Appendix C describes a novel application of TRIM in the development of protective de

minimis emission rates for 14 persistent and bioaccu mulative HAPs (PB-HAPs). We believe that

emissions below de minimis thresholds should not produce unacceptable risks in reasonable worst-case conditions. Facilities emitting below these values would not need to conduct a multipathway exposure and risk assessment.

38Section 3.4 and Appendix I describe a refi ned application of the TRIM model in assessing multi-pathway pollutant transport and its subsequent impacts on human health from Portland cement facility air emissions identified as having a high potential to present significant impacts on human health.

We have limited our developmen t of radionuclide risk estimat es (described in Section 3.2.2 and Appendix G) to those associated with inhalation exposure. Radionuclides were not included in the multi-pathway risk assessment.

4B Is our use of the TRIM model to develop de minimis emission rates appropriate as a screening tool? Are th e methodologies used in the refined multi-pathway assessment consistent with the best available science regarding multi-pathway pollutant transport and human exposures? Are the application of the model and the assumptions us ed clearly articulated? Are

the resultant estimates of medi a concentrations and exposures clearly presented, explained, and interpreted? Given the large uncertainties su rrounding the radionuclide inhalation assessment, are we justified in omitting radionuclides from the multi-pathway assessment?



### Panel Response

Screening model framework and methodologies: In responding to this charge question, the Panel focused on how TRIM.Fate resu lts were applied in the risk assessment process. The Panel did not evaluate the details of the equations in TRIM.Fate and did not itself evaluate the validity of the model. Appendix C desc ribes a series of analyses that provide some confirmation that the screening model result s are generally reasonable based on qualitative comparisons with environmental and food chai n concentrations and partitioning, but these comparisons necessarily fall shor t of providing the leve l of confidence that could be gained by detailed comparison of model results and observati ons for a range of real-world applications. Appendix C indicates that EPA subsequently ev aluated TRIM.FaTE’s pe rformance for modeling mercury and dioxins and furans, but does not discuss the results . As recommended by previous SAB panels, 18 we recommend that EPA continue to identify and acquire additional field data to estimate modeling parameters and to evaluate the TRIM.FaTE model components and other aspects of the modeling system on an ongoing basis. The NRC report Models in Environmental Regulatory Decision Making 19 provides useful guidance for these recommended efforts.

With the caution that continued efforts are needed to evaluate the TRIM.FaTE model, the Panel finds that EPA’s screening approach is based on an appropriate framework and should provide a useful screen for sources that do not need a detailed site-specific multi-pathway analysis. The screening-level multi-pathway assessment is thorough and conservatively includes local subsistence agricultural a nd fishing scenarios, adding exposures across intake pathways to yield total PB-HAP exposure. Children and adul ts are modeled with doses calculated on an

18 EPA-SAB-EC-ADV-99-003 (1998) Advisory on the Total Risk Integrated Methodology (TRIM), http://www.epa.gov/scie nce1/pdf/eca9903.pdf ; SAB-EC-ADV-00-004 (2000) Advisory on the Agency’s “Total Risk Integrated Methodology (TRIM),” http://www.epa.gov/sci ence1/pdf/ecadv04.pdf

. 19 Models in Environmental Regulatory Decision Making. Committee on Models in the Regulatory Decision Process, National Research Council, The National Ac ademies Press, Washington, DC (267 pp, 2007).

39average daily dose (ADD) lifetime basis to assess ch ronic risk of these HAP s. This modeling is generally appropriate, although developmental and reproductive endpoints associated with mercury and dioxin can involve shorter windows of vulnerability than lifetime exposure and so the dose rate averaging might need to be shor ter for such endpoints. Use of an ADD may undervalue peak exposures that occur in earl y life or during pregnanc y. Therefore, some discussion should be provided regarding whet her consideration of early life windows of vulnerability and less than lifetime e xposures should be considered.

Appendix C presents sensitivity analysis result s to identify the most influential input variables in the screening asse ssment. As EPA recognizes, facil ities with emissions exceeding the screening level thresholds might end up doing so because of assumptions in one particular area (e.g., soil to vegetation uptake ra te; beef biotransfer factors; fi sh ingestion). This analysis could be refined so that these particular factors are evaluated in a distri butional sense to enable Monte Carlo analysis, leading to an overall multi- pathway probability distribution of risk rather than a bright line estimate. In this way, the probab ility that a facility’s emissions could lead to unacceptable risk could be estimated and presente d to risk managers to weigh against other

factors.



### Communication of assumptions and results: The Panel considered itreasonable for the Agency to set an emission threshold below which detailed site-specific multipathway analysis (including potentially extensive data collection) would not be necessary for each source. However, the choice of the term “ de minimis ” to describe this threshold was unfortunate, as it obscures the conclusions of the near-source multi-pathway analysis. In particular, when the background concentration of a PB-HAP al ready exceeds a safe level (e.g., where a fish advisory is alre ady in effect) the public may not understand a local source’s contribution being characterized as de minimis . Additionally, although such risk may not be deemed “unacceptable”, it is not clear that a thre shold set at a 1 in 1 million cancer risk or chronic HQ of 1 should be characterized as de minimis in the presence of elevated background

contributions.

Risk assessments must be credible to the public. Exhibit 4-7 showed modeled

concentrations in sediment and surface water for the screening scenario that were higher than

most of the values from the literature. For ex ample, in the screening scenario the modeled

concentration in sediment is about an order of magnitude higher than reported for Minnesota lakes, and Minnesota has a statewide fish a dvisory for Hg. Thus EPA’s finding that the corresponding Hg +2 emissions rate of 1.6E-01 TPY (320 lbs) (Exhibit 2-3) is “below a level of concern” may not be credible to the public.

Instead of “de minimis emissions levels”, it wo uld be better to desc ribe EPA’s screening model results as providing an “action threshold for local hot-spot analysis.” Using a model to

estimate the relative contributions of local and background sources of a pollutant is useful for informing policy choices and communicating with the public. However, the model results need to be clearly presented to show 1) the relative fraction of the local source’s emissions that are deposited locally versus being transported to add to regional burdens, and 2) the relative contributions to total multi-pathway exposure fr om local and regional ba ckground sources. If the

local source contribution is small relative to background, refined site-specific modeling would

40provide little information beyond what could be obtained from a regional or national-scale analysis, so screening out indivi dual sources from further analysis is appropriate. Nevertheless, the contribution the source category makes to ove rall emissions of PB-HAPs should still be

considered.

From a scientific standpoint, EPA must also ensure that ignori ng background pollutant

levels of PB-HAPs does not lead to incorrect results due to nonlinea r physical and chemical processes in the fate and transport model. Wh ere nonlinear processes are at issue, individual

source contributions can be ta gged for tracking, but all contributions including “background” must be considered in the fate and transport model.

Previous SAB review panels have sim ilarly recommended that EPA characterize background as well as incremental risks in its re sidual risk assessments. Quoting from the SAB Advisory on the USEPA’s Draft Case Study Analys is of the Residual Risk of Secondary Lead Smelters (p. 11) “[a] residual risk analysis that does not add expos ures to baseline contamination to the estimates of on-going contamination may vastly underestimate the hazard quotient at the site and incorrectly conclude th at the on-going releases pose risks at less than threshold levels.” 20

The Secondary Lead Smelters review also noted (p. 25) “The [Residual Risk] Report to Congress (USEPA, 1999) 21 discusses the need to in clude background risk and the difficulty associated with this specific issue. ... The absence of an assessment of background risk seriously impacts

statements about the conservative nature of the refined screening assessment.” Our Panel

concurs with these comments. The need to ch aracterize background as we ll as incremental risks

also arises in the case of some non-PB-HAPs such as benzene, but the i ssue stands out for the PB-HAPs because of their nature as persiste nt and bioaccumulative and because for most pollutants evaluated with EPA’s screening scenar io, a large fraction of the emitted mass was lost from the model domain through advection dow nwind (See Exhibit 4-1, Appendix C).



### Omission of radionuclides from the multi-pathway assessment: Local impacts of radionuclides, including naturally occu rring isotopes, need to be considered based on

better data for radionuclide con centrations in geological feed materials to mineral processing industries. The comprehensive analysis presente d in Leenhouts (1996) a nd the results of the Portland cement case study suggest that radionuclide emissions ma y be a risk for any industry category that grinds and heats la rge amounts of natural mineral f eedstock. Radionuclides need to be considered in the residual risk assessment pr ocess, but as discussed in response to charge question 1C, preliminary work is needed before attempting to use TRIM. There is currently no reporting of actual radioactive isotope type and unit of radi oactivity for Portland cement

feedstocks.

At this early stage of the assessment of ra dionuclide emissions, the Panel agrees it is acceptable to omit the multi-media assessment. Ultimately, however, a multi-pathway assesment is needed because non-inhalation pathways are often important for radionuclides that can accumulate in biota and subsequent ly be ingested. Radon is not likel y to bioaccumulate as it is an

20 See EPA-SAB-EC-ADV-00-005 (2000) An SAB Advisory On The US EPA’s Draft Case Study Analysis Of The Residual Risk Of Secondary Lead Smelters

21 See EPA-453/RR-99-001 (1999) US EPA's Report to Congress on Residual Risk

41inert gas, but the fate of its decay products need to be considered. The literature on 210 Po in the

food chain needs to be reviewed to determine if it bioaccumu lates. The literature on multipathway exposure from 210 Po in phosphate fertilizer may provi de information on this issue.



### Particle bound HAPs : A potentially serious omission from the Appendix C analysis

is the issue of HAPs associated with coarse fraction (PM2.5-10 μ m) and very coarse (> 10 μ m) particles. Large particles deposit rapidly, thus causing relatively high impacts near a source. If the HAPs-containing particles are injected into the air near ground level (fugitive emissions and resuspended road dust) then the fraction deposited nearby is much higher compared to the same particles being emitted from a stack. The methodologies used in the case studies would not detect local multi-pathway risk caused by depos ition of particle-bound HAPs near the source

site.



### Charge Question 5

Section 2.2.5 describes our process for deve loping screening and refined estimates of

acute inhalation risk. For acute screening purposes we have assumed that, in the worst case, a person could be exposed for one hour to ten times the highest hourly concen tration calculated by the dispersion model. This in effect assume s a 1-hour emission rate of ten times the annual average (assuming continuous emissions), simultaneous occurrence of “worst-case” meteorological conditions, and also the presence of a person at this worst-case downwind

location.

Appendix B presents an effort to evaluate the protectiveness of this screening assumption using detailed short-term emission data for a limited geographic area. Appendix E describes our

refinement of acute risk estimates for refineries that failed the acute 10X screen, by using more accurate emission points and property boundaries.

Our refined acute assessments do not combine acute hazard quotients associated with

different HAPs because of the inconsistent nature of acute health benchmarks and the inherent conservatism of our exposure assumptions.

5 Does the 10X acute screening assumption for pe troleum refineries appear to be appropriately protective? If not, is it under- or over-protective? Given that this analysis applies only to sources in the Houston area, can we apply the 10X assumption to HAPs in other source categories or should we c onsider some other approach for some other HAPs, e.g., metals? Is there some other way we might address high emi ssion events such as startup or shutdown of processes? Are the refinements to the acute screening assessment objectively employed and scientifically defensible? Should we sum acute hazard quotients by target organ in the same way we do for chronic hazard quotients, i.e., a target organ specific h azard index (TOSHI) approach, or are our reasons for not doing so adequate?

42

### Panel Response

Use of 10X annual emissions for short-term exposure estimates: The Panel agreed there is a critical need for better da ta addressing short-term exposures to HAPs. However, in the absence of chemical- and site -specific data, the use of the 10X screening assumption for petroleum refineries seems reasonable, taking into consideration the assumption

of simultaneous occurrence of ‘worst-case’ meteorological c onditions and the presence of a person at this worst-case downwind location.

While the Panel supports the use of the 10X assumption in the absence of better information, the methods used to derive and jus tify the 10X screening a ssumption are not readily apparent from Appendix B. The authors should consider using a more transparent approach to presenting this data. In revising Appendix B, EP A should at least explai n more clearly why the median and mean values of event to long-term release rates are less than 1. Furthermore, the figures contained in the refere nced reports by Allen et al. pr ovide an easily understandable template that could be used to present the deve lopment of the 10X screening assumption used to assess acute impacts. 22

, 23 Figures 2 through 8 of the Allen et al. paper clearly show the baseline annual hourly emission rates for VOCs, highl y reactive VOCs and 1,3- butadiene, and the magnitudes of the excursions over the baseline annual hourly emission rates. The results

demonstrate that the facilities in the Houston-Galveston area clea rly do not have emissions that are constant and continuous. The daily emissions can vary from the annual average emissions by a factor of 10 to 1000. Figure 1 of the Allen et al. paper also provides a useful conceptual illustration of the four characteristic types of em issions variability from the industrial sources in the Houston-Galveston area which EPA might adapt. 18

Apart from our concerns about data presentation, the Panel c oncurred that a release factor

associated with the 99 th percentile value would seem to be appropriately health-protective. However, there is one significant limitation to the TCEQ database that needs to be identified in Appendix B, which is that the emissions event re porting rule only requ ires reporting from the time of discovery until the event was corrected. This would cause a low bias for both the event duration and quantity of emissions released.

In Appendix B, EPA attempts to address th e representativeness of the Texas Commission on Environmental Quality (TCEQ) data base by f iltering the data to isolate routine and allowable hazardous air pollutant (HAP) excursions from major emitters in the Houston-Galveston area (Table 2 of Appendix B). There appears to be a mixture of sour ce types (e.g., petroleum refineries and chemical manufacturing plants) in Ta ble 2; isolation of petr oleum refinery specific allowable hourly excursion da ta in the manner described above would provide a more transparent justification of the conservative nature of the 10X screening assumption. We are also

22 Allen D, Murphy C, Kimura Y ,Vizure W Jeffries H, Kim, B Webster M and Symons, M. Variable Industrial VOC Emissions and their Impact on Ozone Formation in the Houston Galveston Area. Available at http://www.epa.gov/ttn/chief/confere nce/ei13/uncertainty/allen.pdf

23 Allen D Murphy C, Kimura Y ,Vizure W Jeffries H, Ki m, B Webster M and Symons, M. Variable Industrial VOC Emissions and their Impact on Ozone Formation in the Houston Galveston Area. (April 2004). Final Report

Texas Environmental Research Consor tium Project H-13. Available at http://files.harc.edu/Projects/AirQuality/Projects/H013.2003/H13FinalReport.pdf

43concerned about the filter applied to attempt to remove facilities below the major threshold from this analysis. NESHAP applicab ility for each identifie d facility should be easily obtained from the current Title V permits, the EP A should revisit this filtering as sumption to insure all facilities subject to the NESHAP are included in the analys is. The Panel also suggests that following the screening process, the chemicals of highest concern (drivers) be evaluated against the list of chemicals reported in the Houston area (Appendix B), to ensure they are adequately represented.

The 10X screening assumption makes the most sense under conditions when the production facility is operating c ontinuously (24 hrs/day and 7 days /week) for the entire year. However, adjustments may need to be made fo r other source categories where facilities operate during only part of the day or pa rt of the year. Under these s cenarios, which may frequently occur as demonstrated by the Alle n et al. report and papers, estimat es of daily releases calculated from annual release values may seriously undere stimate releases occurring during production periods. 18, 19

Although the Panel generally ag reed that the 10X assumption could be used for other geographic areas, it was felt that the actual releases w ould be dependent upon the manufacturing processes involved which may or may not be cap tured in the Houston example. There was limited information on the manufacturing proces ses in the Houston ar ea included in the document making it difficult to eval uate its relevance to the case studies. We would recommend that a table listing the industrie s by Standard Industrial Codes (SIC ) be included in the evaluation to allow comparisons with industries to be eval uated in the future. The report by Allen et al. (2004) reports these excursions by SIC code s, allowing for some understanding of source category-specific emissions variability. 2 In the case of petroleum refineries, for example, it appears there are four types of emissions points (fugitives, pipelines, cooling towers and flares)

associated with the short-term excursions.

Going beyond the Houston-Galveston data set, th e Panel suggests that an estimate of the variation and peaks in short-term emission rates could be obtained by examining time trend data

from continuous emission monitors. Since HAPs em itted from a stack are often controlled by the same air pollution equipment that is used for criteria pollutants, it may be reasonable to use variations in PM, SO 2 , or opacity as surrogates for variat ion in emission of HAPs. Another option may be the utilization of real-time fenceline measurements (FTIR or UV-DOAS) collected during enforcement and research inves tigations around facilitie s such as petroleum

refineries.



### Refinements to acute screening assessment: For facilities with elevated acute hazard quotients, EPA used aerial photographs to ensure exposures were not occurring within property boundaries, and re-estimated maximum (off-site) exposures where this had occurred.

The Panel found this to be a useful refinement. In addition, however, where pollutants emerge as drivers of acute risks, EPA should also re-examine the acute toxicity refere nce values used in the

assessment to make sure that th ey are correct and appropriate for the assumed 1-hr period of exposure. For example, the acute REL for benzene used in the case studies (1.3 mg/m 3 ) appears to be based upon a 6-hour exposure pe riod rather than a 1-hr exposure. 24

24 OEHHA (1999) http://www.oehha.ca.gov/air/acute_rels/pdf/71432A.pdf

44

### Summing acute hazard quotients: The Panel recommends that EPA examine the

likelihood that a 10X release w ould occur under the most hazar dous meteorological conditions and how likely it would be for 10X releases of multiple chemicals to occur simultaneously. If it is concluded that simultaneous releases under adverse meteorological conditions would be very unlikely, then summing the acute hazard quotient s by target organ would not be necessary. Alternatively, screening could be done using the TOSHI approach with more detailed follow-up for agents or combinations of agents that were identified as potential concerns. A primary focus of this approach would be on irritants, which ar e generally of most con cern for acute exposures.



### Charge Question 6

Section 3.5 and Appendix J describe a refine d, site-specific application of TRIM to conduct an ecological risk assessment for PB-HAPs emitted by the same Portland cement facility

evaluated in the human health risk assessment. Appendix J also describes a nationwide facility ranking exercise that identifies Portland cement f acilities with the highest potential for causing indirect ecological effects via acidification of the environmen t by hydrogen chloride emissions. Appendix K describes an analysis of possible direct effects on plant foliage of air concentrations of hydrogen chloride emitted from Portland cement facilities that are below human health

thresholds.

6. Is the ecological assessment case study scientif ically defensible? Do es it conform to EPA risk assessment guidance ( e.g. , Guidelines for Ecological Risk Assessment

, Risk Characterization Handbook, etc. )? If not, how can we improve it? Are the elements of the ranking scheme adequate to identify the facilities most likely to be of concern? Are there better data sources or approaches for drawing conclusions for specific locations? With regard to investigating the potential for direct ecological e ffects at air concentrations belo w human health thresholds from other sources or source categories, what sugge stions can be made for prioritizing additional

HAPs for literature searches similar to th at done for hydrogen chloride in Appendix K?



### Panel Response

Ecological risk assessment case study: The ecological risk assessment (ERA) presented in Appendix J is an impressive effort tackling an extremely complex issue. While it is a good first step, the ERA needs to be improved, as th e EPA ERA guidelines were not followed well. This would entail doing a problem formulation stage (which is perh aps the most important stage;

see Dale et al. 2008 25 ) that shows the ecological conceptual model, which then directs the study

[Note: This information can also be found in summary for in the reference ( http://www.oehha.ca.gov/air/pdf/acuterel.pdf ) cited in the text on pages 2-13.]

25 Dale, VH et al. 2008. Enhancing the Ecological Risk Assessment Process. Integrated Environmental Assessment and Management 4:306-313 . [SAB report entitled, “Advice to EPA on Advancing the Science and Application of Ecological Risk Assessment in Environmental Decision Making: A Report of the U.S. EPA Science Advisory Board”, available at: http://yosemite.epa.gov/sab/sabproduct.nsf/7140DC0E56EB148A8525737900043063/$File/sab-08-002.pdf ]

45design, and subsequent linkage be tween assessment and measurement endpoints. In addition, the

risk characterization did not s how how the measurement endpoints linked back to the assessment endpoints and conceptual model. Each stage of the ERA (problem formulation, exposure/effects characterization, and risk charac terization) can be improved, with specific suggestions given

below.

The selection of contaminants of concer n for the ERA case study (Appendix J, section 2.1) is well justified as is the choice of key ecological receptors (Appendix J, section 2.3.1). However, given the paucity of information on othe r potential HAPs, a separate research effort is warranted to rank HAPs for analysis. This effort should consider particulate-associated HAPs, high K

ow compounds, and multiple exposure pathways as shown on the flow charts EPA presented to the Panel to summarize its a pproach for RTR health assessments.

The heavy reliance of the ERA case study on TRIM.FaTE is a concern, as this EPA model has not been well validated in the peer-r eviewed literature for ERAs, and an adequate sensitivity analysis with ground- truthing is lacking. A related concern is the fact that all exposure and effects predictions are based on ge neralized assumptions, and as discussed in response to Charge Question 1A, there are mu ltiple indications that emissions may be underestimated. The potential for error propagation is a concern. More transparency is needed for key parameters used in TRIM.FaTE for the ecological (as opposed to the human health) risk assessment, such as sediment concentrations of the chemicals of poten tial concern and whether or not (and how) their bioavailability is linked to key factors (e.g., tota l organic carbon (TOC), dissolved organic carbon (DOC), and hardness). Appendix I (referenced as the source of the information) is confusing in this regard. It appears virtually all TRIM.FaTE parameters for the test site have been estimated and extrapolated from other sites with a significant amount of “professional judgment”, making this a truly hypothetical ERA. Th is raises the question of how can we assume there is no risk for this, much less other Portland cement f acilities, without some degree of verification that the model’s predictions regardi ng food web, chemical fate and speciation, biological uptake a nd effects are correct?

From the information presented in Appendix J, the case study appears to have relied on Toxicity Reference Values (TRVs) based on data th at are 15 years old or ol der. In addition, it is difficult to determine if, or which, data from Appendix J were used. There have been a multitude of excellent peer-reviewed studies that are releva nt to this process, as they have focused on mercury and highly chlorinated compounds such as di oxins. (Relevant examples are listed at the end of this response.) There are recent data for the Housatonic and Hudson Rivers for TCDD congeners (and related PCBs), that could be further employed to reinforce the assumed

concentrations and feeding patterns.

In Appendix J, section 3.2.3, EPA discusse s and rejects the option of using TRVs expressed in terms of tissue con centrations instead of chemical intake. However, reporting TRVs in terms of tissue concentrations (rather than intake as comm only done for human risk assessments) would allow for more and better comp arisons with the peer-reviewed literature and predictions of risk, as there are fewer peer-reviewed literature reports of intake values. The Agency’s draft RTR document should add tables of these values and calculate new HQs based

46on steady-state tissue concentrations. It would also be helpful to see predicted concentrations in sediment and sediment quality guidelines listed in the same table.

In the case study, EPA uses a two-stage appr oach to characterize ecological risks from Hg and dioxin emissions. In the first stage, ri sks are summarized by computing hazard quotients as the exposure dose divided by the TRV (Appe ndix J, section 3.2.4). The Hazard Quotient approach is justifiable as a crude screening le vel approach in applications such as the RTR assessments, but only if very conservative values ar e utilized. For ground-tr uthing this effort, or for a refined ERA, probabilistic approaches ar e needed. We know Hazard Quotient-type ERAs are fraught with unacceptably high levels of unc ertainty regarding expos ures and their linkages

to adverse effects and do not account for multi-stressor and non-chemical stressor interactions and resulting effects. The assumptions of constant exposures are of course conservative, but best used in a “reference condition” approach whereby multiple reference sites within the area of the facility are considered. This w ill help account for the non-facility related exposures and effects. For a more thorough discussion of these issues and others importan t to improving the ERA process, see Dale et al’s ( 2008) summary of their recent EP A SAB report (EPA-SAB-08-002). 18

For ecological risk, there are some overlappi ng charge questions w ith the human health

risk assessment that should be considered. In particular, Charge Questions 2 and 3, concerning dispersion modeling and dose-response assessme nt, affect both risk assessments. The environmental chemistry (atmospheric chemistry) and fate are critical for ecological assessment endpoints to be determined. For example, mo re consideration needs to be given to how particulate matter may interact with certain type s of chemicals in the emissions. In particular, coarse particulates that settle within 1 to 2 miles of the site may contain high levels of contaminants and should be considered as a potential exposure compartment. High K

ow compounds, such as PAHs and dioxins, will adsorb to carbon, so the presence of particulate matter may be critical in bioava ilability and fate. QSAR (Qua ntitative structure and activity relationship) models are important in this respect, as is the nature of the ecosystem into which the chemicals and particulate matter deposit. Although the TRIM-FaTE model si mulations indicate little e xpected risk to humans via inhalation, other receptor organisms, such as bent hic macroinvertebrates and fish in waters or soil invertebrates may be affected.

On pages J-29 through J-33, EPA presents a sens itivity analysis of how angler harvesting would affect mercury and dioxin concentrations in food web compartments for the ERA case study. This analysis should be omitted, as fish harv esting by fishermen should not be a part of an

ERA.

In characterizing the risk for the Ravena case study (page J-46), EPA discounts the finding of elevated HQ values for Ravena pond on the grounds that it is a small water body with correspondingly small wildlife populations. The ra tionale that no population effects will occur in a small water body because there are few individuals is flawed. In fact, small water bodies with small populations may pose special concerns, as has been shown in prior studies. Greater population effects would be expected in systems with fewer indivi duals, particularly with limited to no refugia for recruitment. Page J-46 also indicates no adverse effects are expected for

47piscivorous and insectivorous wildlife, even though they have elevated HQs. The stated conclusions cannot be justifie d using the HQ approach.

The assumption that ecological receptors will be protected if human h ealth is protected is incorrect. Recall the “canary in the coal min e” approach was derived long ago and it is well known that wildlife are good sentin els for protecting humans due to their greater sensitivity. Through literature comparisons it should be possi ble to develop a sound “safety/application” factor that protects species of concern (note mink/ot ter will likely be the species of greatest risk).

This literature based factor could then be us ed to back calculate (v ia TRIM-FaTE) to an allowable emissions concentration, which would f it nicely into the existing flowchart replacing the top decision point based on no human health effects.



### Facility ranking scheme: The process to select the Portland cement facilities of greatest potential concern for HC l deposition using pH, hardness, alkalinity and so il type data was very good. For site-specific ERAs, however , other site characteri stics should also be considered, such as altitude, gradient, troph ic status, TOC levels, watershed location (e.g., headwaters), sensitive land uses (forested, protected areas, wetlands) , and sensitive, threatened or endangered receptors (e.g., amphibians, mussels, piscivorous wildlife). For the discussions on Hg, the trophic status of the re ceiving lake or pond becomes impor tant. Methylation of Hg is very site dependent. For example, it tends to be stronger in lakes with hi gh organic matter in the sediments. Over time, much of the terrestrial primary production moves to the aquatic habitats in watersheds. Hence, the buildup of organic materi als in shallow riparian habitats influences the bioavailability of chemicals deposited. If the RT R process is to establ ish a guide for ERAs conducted under the Clean Air Act, there may be value in adding a sectio n on the importance of obtaining, for each site, site-speci fic emissions and exposure data. Otherwise, it will be difficult to account for the wide range of cr itical factors that will affect ecological risk and are defensible

in a court of law.



### Direct-contact ecological assessments: EPA explains it has not developed

criteria for HAPS for direct-cont act ecological assessments (pag e 3-20), yet there was an RTP workshop 3 to 5 years ago [Federal Regist er Notice announcement published September 8, 2005 (Volume 70, Number 173,Page 53360)] with the focus of bringing the ERA process into emissions of HAPs. There were a lot of good ideas put forward that should be considered for the RTR assessments. There should be a peer-reviewed effort to reevaluate other potential HAPs of ecological concern, particularly those that associated with pa rticulates, from both petroleum refinery and Portland cement operation emissions.

In summary, many of the above concerns and issues can be addressed by conducting a ground-truthing ERA at a site such as the Ra vena Pond, or by a comparison of TRIM.FaTE, predictions with more conve ntional ERA methods (e.g., using Bioaccumulation Sediment Accumulation Factors in food web models (e .g., TrophicTrace and EcoFRAM by Frank Gobas, USEPA’s AQUATOX 2.2, CATs) at a well studied site with similar CoCs (e.g., see web sites for USEPA reports on Superfund sites: Lower Housatonic River, Region I ( http://www.epa.gov/NE/ge/pcbshealthandenviro.html ); Hudson River, Region II ( http://www.epa.gov/hudson/reports.htm ); Fox River, Region V ( http://www.epa.gov/Region5/ sites/foxriver/index.html )). This could be done by a contractor

48experienced with ERAs and they could use mo re conventional fate/effect ERA models using both deterministic and probabilistic approaches with limited on-site sampling of exposure compartments and receptors. Sediment concentr ations of the CoCs can be linked to food web bioaccumulation and then compared to adverse tissu e levels in the key receptors. This rather simple effort would determine whether the pr oposed generalized approach works and is of sufficient accuracy to warrant it s nationwide application. This w ould allow for refinement of the “nationwide” Tier 1 ERA approach and with gene ral guidelines for site-specific, Tier 2-type

evaluations.

References that may be relevant as EPA review s its approach for ERA in the RTR process are provided in Appendix A.



### Charge Question 7

The risk characterizations for these two case st udies (Sections 2.3 and 3.6) represent our current practices in providing informa tion to decision-makers responsible for RTR rulemaking. The analyses presented in the appendi ces are by and large illustrative of what can currently be done in the regulatory context, given time and resource constraints.

7 Do these characterizations objectively and comp letely incorporate the goals and principles of

EPA’s Risk Characterization Handbook to the extent scientifically feasible? In particular do they provide a complete and transparent discussi on of uncertainties and limitations? If not, how can the risk characterizations be improved? Can you suggest wher e we might focus any

additional efforts and resources in order to have the biggest impact on refining risk characterizations for these RTR assessments, ultimately leading to bett er regulatory decisionmaking?



### Panel Response

Risk characterizations are often difficu lt to develop because a highly technical assessment must be communicated to decision makers and others who may lack some of the underlying technical background. As stated in the Risk Characterization Handbook (pg 13):

“Are Risk Assessment and Risk Characterization the Same? No, they’re not the same. Risk assessment is a process comprised of several steps (see section 1.2.1 above for detail). Risk char acterization is the culminating step of the risk assessment process. Risk char acterization communicat es the key findings and the strengths and weaknesses of the assessment through a conscious and deliberate transparent effort to bring all th e important considerations about risk into an integrated analysis by being clear, consistent and reasonable".

The Agency's draft RTR document took gr eat care in summarizing and providing justification/explanation for most of the results, including atte ntion to uncertainties. The summary tables (tables 2-7 and 3-3) were well d one and provide a concise summary of the risk

49assessment results for the risk manager. Howe ver, a number of improvements are possible.

Substantive issues are discussed in the remainde r of this section, with editorial suggestions provided in Appendix B.



### Presentation of risk characterizations : In the RTR case studies, the presentation
of methods, risk assessment result s, and risk characterization ar e intermingled, such that the purposes of the risk characterization are not met. This can be improved by focusing more on the purpose of the characterization to communicate with decision makers as the primary audience, recognizing that transparency is important and th at the audience will inevitably be broad (e.g., a reporter may use it as a source for a story, th e regulated source may use it for community interaction). To these ends the Panel recommends the following improvements:

1. Develop a separate methods document that contains a full description (including uncertainties) of all of the common components of the source-specific risk assessments. For example, it would include EPA RfC and can cer assessment methodologies, the National Emissions Inventory (NEI) description, AEGL methods, etc.

2. Refer back to this master document, as appropriate, in source-specific risk characterizations, while providing additional inform ation particular to the source category at issue. For example, in a source-specific risk characterization, there is no need to repeat a

discussion of mode-of-action for can cer risk if it wasn’t used. On the other hand, source-specific

discussions of uncertainties are far more useful than generic bo ilerplate about uncertainties. For example, there may be particularly strong (or we ak) elements of the emissions inventory that need to be discussed for a specific source.

3. While other sections of the RTR assessments should document the technical details, the

risk characterization sections should stand alone. A broad outlin e of the risk characterization

section would include:

a. The general background information for th e RTR assessment (perhaps using flow diagrams).

b. The risk characterization, with sections on emissions, cancer risk, non-cancer risk, and ecological risk, each of which integrate results and uncertainties and are written for

EPA decision-makers. For HAPs that are found to drive risks, the risk characterization should include expanded discussion of the na ture of the effects at issue (including qualitative cancer classification if applicable ) and potential suscepti bilities (e.g., children, elderly, women of childbearing age, individual s with preexisting dise ases). For example, page 3-23 says that the maximum hazard i ndex for Portland cement manufacturing is associated with potential effects of mangane se compounds on the central nervous system. But what type of CNS effects are they? What groups are expected to be more susceptible? Expanded discussi on is important to understand ing the “real-world” risk, including dealing with health disparities. Fo r example, it would be important to recognize if a risk driver for a particular facility exacerbated asthma and the community surrounding the facility was a low-income popul ation with an elevated incidence of

asthma.

c. A summary with a clear description of risks of concern, using language

understandable to an educated non-technical audience. This section should be relatively

50brief and balance the weight-of-the-evidence. An example audience for this section might include officials in the community wher e a facility of concern is located; they

should be able to understand th e nature of and degree of ri sk to human and ecological

health.



### Risk characterization for facilitie s covered by more than one source
category : The Clean Air Act requires residual risk a ssessment of source categories, which have a particular definition that may only include pa rt of a facility. For example, the petroleum refinery MACT 1 standards do not cover combus tion processes within a refinery facility. Although this requirement for separate assessments has practical regulator y implications, it only partially accounts for potential human health or ecol ogical risk. Since regulators seek protection of the public health and the environment, the risk characterization s hould clearly explain the inherent limitations of only d ealing with one source category at a time. This limitation needs to be clearly noted for the risk manager. This will not change the source risk characterization itself. However, it can change its interpretation, especial ly in the case of large industrial complexes. For example, the Coke Oven Residual Risk Assess ment clearly identifies that it is assessing a source category (i.e., coke batteries) that is only part of an entire facility. 26 The risk assessment provides the estimated risk associated with emi ssions from the subpart and also accounts for similar emissions from different processes at th e plant to provide the risk manager with an estimate of the total facility ri sk in the surrounding community. The risk characterization section should provide an estimate of total facility risk for facilities subject to multiple federal emission standards for hazardous air polluta nts or clearly identify it as an outstanding issue that needs to

be examined further.



### Characterization of aggregate and cumulative risks: Since risk will be influenced by aggregate and cumulative e xposures, finding that a source category has no significant risk from a particular chemical or a mixture of chemicals does not mean that people in

the area are without risk from that chemical or mixture. For exam ple, Houston faces particularly difficult air toxics challenges due to the signi ficant air emissions from one of the largest petrochemical complexes in the world. There ar e more than 100 benzene sources alone from refineries and chemical plants in the Houston area. Harris County, in which Houston is located, over 19 million pounds of hazardous air pollutants were emitted in 2003, including 750,325 pounds of benzene according to the EPA’s 2003 Toxic Release Inve ntory (TRI) report. From a public health viewpoint, personal exposures re sulting from occupationa l or behavior (e.g., smoking) sources can also contri bute to risk beyond that of a particular source category.

Ecological examples where aggregate risks are important also exist. The PB-HAP methodology used for the case studies does not consider background conc entrations, focusing

instead on incremental risks from the source cate gory. However, ecological resources are also influenced by aggregate and cumulative exposure that must be considered in protecting the

environment. The ecological receptors, just as humans, are affected by their total environment and all the stressors to which they are exposed. Exposures to multiple st ressors at sub-lethal levels can result in lethal effects. Since many of these facilities will be located in human-

26 United States Environmental Protection Agency (USEPA) 2005a. USEPA Risk Assessment Document for Coke Oven MACT (maximum achievable control technology) Residual Risk – March 31, 2005

51dominated watersheds, there is a high probability that organisms will be exposed to multiple

stressors.

Linkage of Hazardous Air Pollution Emissi ons and Risk Assessment: RTR assessments must provide clear documentation of the hazardou s air pollutants emissions that are modeled in the risk assessment. For example, the RTR case study models actual emissions using the 2002 National Emissions Inventory (NE I) and there apparently was an adjustment of these emissions using site-specific data from 22 refineries as provided by the American Petroleum Institute. However, it is not clear what adjustments were made.

The risk characterization for petroleum refine ries includes a discu ssion of an ingestion pathway screen for POM emissions and indicates all 156 facilities were screened. However, one important aspect of this screening was not adequa tely explained. Only 70 facilities reported some type of POM emissions (Table 2-6) and the POM emission rates used to assess the potential risk for 156 facilities are never explai ned to the risk manager. Th e emission summary tables should

include the emissions that were modeled to es timate cancer and non-cancer risk for the inhalation and ingestion pathways.



### Identification and discussion of uncertainties : RTR assessments must proceed even though most will have a relatively long list of uncertainties. Such a list should be treated as an opportunity to identify future improvements. In sofar as possible, the Panel recommends that EPA perform a sensitivity analysis to identify the major uncertainties and then proceed to: (1) explain them clearly in the risk characterization section and (2) take steps to reduce them. For example, it appears that the NEI and paucity of up-t o-date IRIS values are very likely to have a significant impact on the quality of the RTR assessments. These problems should be emphasized more, and EPA management should seek improvements so that future assessments can benefit. Action on major uncertainties that ca n be identified very early in the assessment of a particular source could have a substantial impact on the utility of that a ssessment. For example, if an apparent chemical driver has an out-of-da te (or no) health value, it may be possible to rectify this problem prior to completion of the assessment.

In the case studies, EPA has generally done a good job of investigating uncertainty in many aspects of the inputs for the residual risk as sessments. The sensitivity analyses provided to the Panel are extensive. However, the next step is to carry the results of these uncertainty analyses through to the final risk results and ch aracterization. There are too many sources of uncertainty to qualitatively brush aside differe nces with statements discounting degrees of uncertainty because of either the risk range or that the component in question “does not introduce significant uncertainty into the risk assessment rela tive to other sources of uncertainty that limit reporting risk estimates to one significant figure” (page 4-7).

As one example, on pages 2-21 and 2-27, the concern is raised that Canadian and European studies [30, 31] suggest emissions from some refineries are significantly higher than amounts estimated, but this is acknowledged almost in passing in the uncer tainty section (page 2- 30). This is an important issue for the human and ecological risk assessm ent and if it cannot be accounted for, then appropriate uncertainty factors must be used.

52

### Ecological risk characterization: According to USEPA ERA guidelines, the Risk Characterization should link measurement endpoint effects back to the assessment endpoints and conceptual model, which the Agency's draf t RTR document does not do. Furthermore, as discussed in the response to Charge Question 6, no site-specific data exist for the ERAs, with every aspect of the ERA from e xposure to effects (thus the risk characterization) being based on non-site data averaging, assumptions, questio nable extrapolations, averaged/steady state conditions, and literature-based values. It seem s that the only ways to get past the huge

uncertainties involved are to do some case studies w ith site specific data that would represent a Tier 1 ERA, or to compare the TRIM.FaTE pred ictions to those of another peer-reviewed study (e.g., studies conducted at Superfund sites such as the Lower Housatonic River, Fox River, or Hudson River). After those efforts are complete d, a guidance document could be provided that explains the general ERA process for the Tier 1 ex ercise, the most critical input parameters to determine if a site risk may exist (e.g., sensitive ecosystems/receptors, high emissions), and suggest a more refined Tier 2 ERA process following USEPA ERA guidelines for reducing uncertainties. In addition, the public will be susp icious of the assumption that petroleum refinery emissions are not an ecological ri sk issue, so a more thorough jus tification is needed with site specific documentation.

A few strong contentions re garding ecological risks n eed more discussion and justification. For example, pa ge 3-20 (second paragraph under 3.5.1) contends that if there are no adverse effects on humans, the “potential for adverse environmental effects...was considered to be insignificant. This assumption needs some scientific justification. Also, the rationale (e.g., pages 3-22 and J12 (J-3.2.4)) that no population effects will occur in a small water body because

there are few individuals is fl awed, and could be the reverse. Fewer individuals (lower abundance) means the population is more susceptible to extinction, particul arly if there are few to no areas of refugia for recruitment of new individuals.

On page 2-29 it is stated that contaminan t concentrations were evaluated against ecological benchmarks for sediment, soil and wa ter. These comparisons were not found and must be reviewed. Which benchmarks (there are many for sediments)? What concentrations were used for each media? What was their associated uncertainty?



### Cancer risk characterization: In Table 2-6 on page 2-22, the blank space for

toluene cancer unit risk stands out because to luene has the greatest emissions. A person scanning this table would worry that this unknown could be a great source of risk. The document should explicitly discuss toluene cancer studies (from IRIS) and risk classification in the earlier section on dose-response. It should also provide a summary in the text here about the evidence being inconclusive, but since good studies were performed carc inogenic effects would likely have been observed if the risk were high. Thus, it cannot be dismissed, but there is no current evidence for significant concern.

In the discussion of uncertainties in dose -response relationships for cancer assessment, the most important uncertainty is probably that the upper bound is used for assessments. The discussion of this issue in the last paragra ph of page 2-32 is good. Page 2-33 describes the cancer guidelines accurately. However, with pe rhaps one exception, defaults were used. For example, on page 2-33, the paragraph dealing with pharmacokinetic models is accurate, but not

53relevant if none of the URE values were ac tually developed or modified through such an analysis. If they were not, this information s hould be deleted and only in cluded in case studies where it was used, specifying the chem ical for which it was used.



### Characterization of chronic non-cancer risks: This section has about two full pages of description of the Rf C/D methodology (pages 2-36 on). It should be reduced by about half, only providing information pertinent to understanding whic h uncertainties applied to the particular source category and which were accounted for in the RfC/D derivation. A full description of the methods is mo re appropriate for a separate general methods document. Some

of the discussion here is redundant . A simple description will co mmunicate the process better. The focus should be on the RfC, with a brief pa ragraph explaining where the RfD differs. Right now, much of the text treats th e RfD as the “standard”, when it doesn’t apply (e.g., dosimetric adjustment). There are also a few missing elem ents or errors in th e description of the methodology, which are described below.

a. Insert a sentence that states that the RfC has basi c data requirements (e.g., at least a 90 day study, etc) before proceeding, to e xplain that an RfC is not guesswork.

b. Insert a short discussion about how many of th e uncertainty factors (UFs) have redundant

elements and therefore are conservative when multiple UFs are used. That is why EPA

has a maximum of a total factor of 3000.

c. On page 2-37, under paragraph “1)”, note that the heterogeneity UF includes children, people with preexisting disease, and ot her populations that may have added susceptibility. This is implied in the word “het erogeneity,” but it is im portant to be direct

about such a critical risk element.

d. On page 2-37, paragraph “2)” needs signi ficant revision. The RfC methodology for extrapolation from animals to humans includes a dosimetric adjustment, resulting in the

routine use of an UF of 3 to account for pharmacodynamic extrapolation. The RfD methodology does not do this routinely. This is a major difference. The paragraph implies that an UF of 10 is routinely use d. Also, the paragraph talks about mg/kg/day which is only relevant to the RfD.



### Characterization of acute health risks: The discussion on page 2-38 should be expanded to cover the uncertainties involved in the values chosen (e.g., AEGLs, ERPG). This is especially important since these are levels that cause effects, rather than “safe” levels. The

discussion in this section should bett er parallel the section on chronic risks. The difficulty is that acute exposures did not really contribute much risk in the cas e study, but it still bears more

discussion.

54

### APPENDIX A

References that may be relevant as EPA revi ews its approach to eco logical risk assessment

Bargar TA, Scott GI and Cobb GP. 2001. Maternal transfer of contaminants: Case study of the excretion of three polychlorinated biphenyl conge ners and technical grade endosulfan into eggs by white leghorn chickens ( Gallus domesticus ). Environmental Toxicology and Chemistry 20:61-

67.

Brasson RL and Cristol DA. 2008. Effects of merc ury exposure on the reproductive success of tree swallows ( Tachycineta bicolor ). Ecotoxicology 17:133-141

Custer TW and Heinz GH. 1980. Reproductive succe ss and nest attentiveness of mallard ducks

fed Aroclor 1254. Environmental Pollution (Series A) 21:313-318.

Custer TW et al. 2002. Dioxins and congener-speci fic polychlorinated biphenyls in three avian species from the Wisconsin River, Wisconsin. Environmental Pollution . 119:323-332.

Fernie KJ, Smits JE, Bortolotti GR, and Bi rd DM. 2001. Reproductive success of American kestrels exposed to dietary polychlorinated biphenyls. Environmental Toxicology and Chemistry

20:776-781.

Hochstein JR, Bursian SJ, Aulerich RJ . 1998. Effects of dietary exposure to 2,3,7,8- tetrachlorodibenzo-p-dioxin in ad ult female mink (Mustela vison) , Archives of Environmental Contamination and Toxicology , 35(2), 348-53.

Ludwig JP, Kurita-Matsuba H, Auman HJ, L udwig ME, Summer CL, Giesy JP, Tillitt DE and Jones PD. 2009. Deformities, PCBs, and TCDD-E quivalents in Double-Crested Cormorants ( Phalacrocorax auritus ) and Caspian Terns ( Hydroprogne caspia ) of the Upper Great Lakes 1986–1991: Testing a Cause-Effect Hypothesi s. Available online 23 February 2009.

McCarty JP and Secord AL. 1999. Repr oductive ecology of tree swallows ( Tachycineta bicolor ) with high levels of polychlorin ated biphenyl contamination. Environmental Toxicology and Chemistry 18:1433-1439.

Nosek JA, Craven SR, Sullivan JR, Olson JR and Peterson RE. 1992. Metabolism and disposition of 2,3,7,8-tetrachlorodi benzo-p-dioxin in ringnecked ph easant hens, chicks, and eggs. Journal of Toxicology and Environmental Health 35:153-164. Rice CP et al. 2003. Souces, Pathways, and Effect s of PCBs, Dioxins, and Dibenzofurans. In, Hoffman DJ et al. (eds), Handbook of Ecotoxicology, 2 nd edition. CRC Press. Boca Raton FL. Pp 501-573. (a critical review article)

Tanabe S, Subramanian A, Hidaka H and Tatsuka wa R. 1986. Transfer rates and pattern of PCB isomers and congeners and pp-DDE from mother to egg in Adelie penguin ( Pygoscelis adeliae ). Chemosphere 15:343-351.

55Tillitt DE, Gale RW, Meadows JC, Zajicek JL, Peterman PH, Heaton SN, Jones PD, Bursian SJ, Kubiak TJ, Giesy JP, and Aulerich RJ

. 1995. Dietary Exposure of Mink to Carp from Saginaw Bay. 3. Characterization of Dietary Exposure to Planar Halogenated Hydrocarbons, Dioxin Equivalents, and Biomagnification. Environ. Sci. Technol. , 30 (1), pp 283–291.(Publication Date (Web): December 27, 1995).

Wiener JG et al. 2003. Ecotoxicology of Mercur y. In, Hoffman DJ et al. (eds), Handbook of Ecotoxicology, 2 nd edition. CRC Press. Boca Raton FL. Pp 409-463. (a critical review article)

5657 

### APPENDIX B

Editorial suggestions for risk characterization sections:

1. As described in the EPA document, during th e risk characterization step, information from other risk assessment steps is integrated to come to an overa ll conclusion about the risks involved. As a result, for the petrol eum refinery discussion, Section 2.3.2 and the

information in Tables 2-7 and 2-8 should be included in the Risk Characterization section. Similarly for the Portland cement discussion, Section 3.3 and the information

contained in Tables 3-3 and 3-4 should be in cluded in the Risk Characterization section. The details of EPA’s cancer guidelines for early -life exposure (page 2-17, last paragraph) should be moved to section 2.2.6 on dose-respon se assessment. The risk characterization should provide some of this information, but de lete the details for the age groups and also the BaP equivalence.

2. The risk characterization should “stand alone”. For example, in some cases abbreviations are used excessively for the intended audien ce. All but very co mmon abbreviations (e.g. HAP) should be avoided. For example, on page 2-19, “TOSHI” should be spelled out.

The abbreviation URE should be defi ned on page 2-17, in paragraph 3.

3. On page 2-22, in Table 2-6, in the first row, specify that the URE is the upper bound (perhaps through a footnote).

4. The footnotes often provide excessive detail for the intended audience. Perhaps they could be summarized in plain English, with references provided for those seeking the precise words. Footnote 29 might be omitted.

5. P2-18ff. Section 2.2.7.2 Mixtures. P2-19, lin e 1. The word aggregate should be changed

to cumulative since the intent here is to look at mixtur es of different chemicals.

6. P 2-26 Table 2-8. Consider addi ng a footnote that defines the term “refined” used in the

title.

7. P2-32, Section 2.4.2. Para 1 The description of durations not used c ould be deleted (i.e., just keep the descriptions fo r 1 hr and chronic durations).

8. P2-35. Last paragraph, “Chronic noncancer...” afte r the word represent, delete “chronic” and insert “70-year lifetime continuous e xposure”. Since everyone knows that such exposure scenarios are highly unl ikely, the reader will automatic ally sense an uncertainty

in the conservative direction.

9. P2-36 paragraph 3, line 3. Delete “relevant” and insert “sensitive” after endpoint. It is the “critical” endpoint, but such language doesn’t really communi cate effectively.

10. P2-38 para 1. Line 4. Insert “respiratory” before irritation.