1



## Residual Risk Assessment for the Wood Furniture Manufacturing Source Category

by EPA’s Office of Air Qualit y Planning and Standards

Office of Air and Radiation August 20112



### Table of Contents

1 Introduction ........................................................................................................................ 4

2 Methods

.............................................................................................................................. 5

2.1 Emissions and source data ......................................................................................... 5

2.2 Dispersion modeling for inha lation exposure assessment ......................................... 5

2.3 Estimating human inhalation exposure ...................................................................... 8

2.4 Acute Risk Screening and Refined Assessments ....................................................... 8

2.5 Multipathway and environmental risk screening ..................................................... 10

2.6 Dose-Response Assessment ..................................................................................... 11

2.6.1 Sources of chronic dose-response information ................................................ 11

2.6.2 Sources of acute dose-response information .................................................... 17

2.7 Risk Characterization ............................................................................................... 21

2.7.1 General ............................................................................................................. 21

2.7.2 Mixtures ........................................................................................................... 23

2.7.3 Facility-wide Risks .......................................................................................... 24

3 Risk Results for the Wood Furn iture Manufacturing Source Category ....................... 24

3.1 Source Category Descriptions and Results .............................................................. 24

3.2 Risk Characterization ............................................................................................... 28

4 General Discussion of Uncertainties and How They Have Been Addressed ................... 35

4.1 Exposure Modeling Uncertainties ............................................................................ 35

4.2 Uncertainties in the Dose-Response Relationships .................................................. 36

5 References ........................................................................................................................ 44

Index of Tables

Table 2.2-1 AERMOD version 09292 model options for RTR II modeling ............................ 6 Table 2.6-1 (a) Dose-Response Values for Ch ronic Inhalation Exposure to Carcinogens ..... 14 Table 2.6-1 (b) Dose-Response Values for Chronic Oral Exposure to Carcinogens .............. 15 Table 2.6-1 (c) Dose-Response Values for Chr onic Inhalation Exposur e to Noncarcinogens 15 Table 2.6-2 Dose-Response Values for Acute Exposure ........................................................ 20 Table 3.1-1 Summary of Em issions from the Wood Furniture Manufacturing Source Category and Availability of Dose-Response Values .............................................................. 25 Table 3.2-1 Summary of Sour ce Category Level Inhalation Risks for Wood Furniture Manufacturing Facilities .......................................................................................................... 29 Table 3.2-2: Summary of NP RM Acute Refined Results for Wood Furniture Manufacturing

Facilities

.............................................................................................................................. ..... 30

Table 3.2-3 Wood Furniture Sour ce Category Contribution to Facility-Wide Cancer Risks . 353

Appendices

Appendix 1 Emissions Inventory Support Memorandum Appendix 2 Technical Support Docu ment for HEM-AERMOD Modeling Appendix 3 Meteorological Da ta for HEM-AERMOD Modeling Appendix 4 Analysis of Data on Short-Te rm Emission Rates Relative to Long-Term

Emission Rates Appendix 5 TRIM-Based Multipathway Screening Scenario Appendix 6 Detailed Risk Modeling Results (Source Category and Facility-wide) Appendix 7 Refined Acute Assessments

Index of Acronyms

AERMOD American Meteorological Society/EPA Regulatory Model AEGL Acute exposure guideline level ASTDR US Agency for Toxic Substances and Disease Registry CalEPA California Environmental Agency ERPG Emergency Response Planning Guideline

HAP Hazardous Air Pollutant

HEAST Health Hea lth Effects Assessment Summary Table HEM Human Exposure Model

HI Hazard index HQ Hazard quotient IRIS Integrated Risk Information System MACT Maximum Achievable Control Technology

MIR Maximum Individual Risk

MOA Mode of action NAC National Advisory Committee

NATA National Air Toxics Assessment NEI National Emissions Inventory NPRM Notice of Proposed Rulemaking

PB-HAP Persistent and Bioaccumulative HAP POM Polycyclic organic matter REL Reference exposure level

RfC Reference concentration

RfD Reference dose RTR Risk and Technology TOSHI Target-organ-specific hazard index

URE Unit risk estimate4



### 1 Introduction

Section 112 of the Clean Air Act (CAA) establ ishes a two-stage regu latory process for addressing emissions of hazardous air pollutants ( HAPs) from stationary sources. In the first stage, section 112(d) requires the Environmental Protection Agen cy (EPA, or the Agency) to develop technology-based standards for categorie s of sources (e.g., petr oleum refineries, pulp and paper mills, etc.) [ 1 ]. EPA has largely completed the initial Maximum Achievable Control Technology (MACT) standards as re quired under this provision. Under section 112(d)(6), EPA must review each of these tec hnology-based standards at least every eight years and revise a standard, as necessary, “tak ing into account developments in practices, processes and control technologi es.” In the second stage, EPA is required under section 112(f)(2) to assess the health and environmental risks that remain after implementation of the

MACT standards. If additional risk reductions ar e necessary to protect public health with an ample margin of safety or to prevent an a dverse environmental effect, EPA must develop standards to address these remaining risks. This second stage of th e regulatory process is known as the residual risk stage. For each source category for which EPA issued MACT standards, the residual risk st age must be completed within eight years of promulgation of the initial technology-based standard.

In December of 2006 we consulted with a panel from the EPA's Science Advisory Board (SAB) on the “Risk and Technology Review (R TR) Assessment Plan” and in June of 2007,

we received a letter with the results of that c onsultation. Subsequent to the consultation, in July of 2009 a meeting was held with an SAB panel for a formal peer review of the “Risk and Technology Review (RTR) Risk Assessment Methodologies” [ 2 ]. We received the final SAB report on this review in May of 2010 [ 3 ]. Where appropriate, we have responded to the key messages from this review in developing our current risk assessments and we will be continuing our efforts to improve our assessm ents by incorporating updates based on the SAB recommendations as they are developed and be come available. Our responses to the key recommendations of the SAB are outlined in a memo to this rulemaking docket [ 4 ].

This document contains the methods and results of baseline risk assessments (i.e., after the implementation of the respective MACT sta ndards) performed for the Wood Furniture Manufacturing source category. The methods disc ussion includes descrip tions of the methods used to develop refined estimates of chronic inhalation exposures and human health risks for cancer and non-cancer endpoints, as well as descriptions of the methods used to screen for acute health risks, chronic non- inhalation health risks, and adverse environmental effects. Since none of the screening assessments indi cated any significant potential for chronic noninhalation health effects or e nvironmental impacts, includi ng effects to threatened and endangered species, no further refinement of these assessments was performed.5



### 2 Methods

2.1 Emissions and source data

The 2005 National-Scale Air Toxics Assessmen t (NATA) National Emissions Inventory (NEI) served as the starting point for this as sessment. The 2005 NEI contains information on actual emissions during the entire 2005 base year. Using the process MACT codes 1 , we developed a subset of this inve ntory that contains emissions a nd facility data for the source category. Next, we performed an engineering re view of the information using EPA engineers who were directly involved in the developm ent of the MACT standards for the source category, and/or who have extensive knowledge of the characteristics of this industry. NEI data were updated with available industry supp lied data. The goal of the engineering review was to identify limitations and issues with the emissions data (particularly those that would have the potential to influence risk estimates) and to make changes to the data where possible

to address these issues and decrease the un certainties associated with the assessment.

Details on the development of the emissions a nd source data for this source category are

discussed in Section 3. The emissions data and modifications made to the NEI data are discussed in Appendix 1, entitled “Emissi ons Inventory Support Memorandum.”



### 2.2 Dispersion modeling for inha lation exposure assessment

Both long- and short-term inhalation exposure co ncentrations and associat ed health risks from each facility in the source category of interest were estimated using the Human Exposure

Model in combination with the American Meteorological Society/ EPA Regulatory Model dispersion modeling system (HEM-AERMOD). Th e approach used in applying this modeling system is outlined below, and further deta ils are provided in Appendix 2. HEM-AERMOD performs three main operations: atmospheric dispersion modeling, estimation of individual human exposures and health risks, and estimatio n of population risks. This section focuses on the dispersion modeling component. The expos ure and risk characterization components are

discussed in other subsecti ons of Sections 2 and 3.

The dispersion model in the HEM-AERMOD sy stem, AERMOD version 09292, is a state-ofthe-science Gaussian plume di spersion model that is prefer red by EPA for modeling point, area, and volume sources of continuous ai r emissions from facility applications [ 5 ]. Further details on AERMOD can be found in the AERMOD Users Guide [ 6 ]. The model is used to develop annual average ambient concentra tions through the simulation of hour-by-hour dispersion from the emission sources into the surrounding atmosphere. Hourly emission rates used for this simulation are generated by evenly dividing the total annual emission rate from the inventory into the 8,760 hours of the year.

1 The tagging of data with MACT codes allows EPA to de termine reductions attributable to the MACT program. The NEI associates MACT codes corresponding to MACT source categories with stationary major and area source data. MACT codes may be assigned either at the pro cess level or at the site level in the point source data (e.g., the MACT code for municipal waste combustors (M WCs) is assigned at the site level whereas the MACT code for petroleum refinery catalytic crack ing is assigned at the process level).6

The first step in the application of the HEM-AERMOD modeling system is to predict ambient

concentrations at locations of interest. The AERMOD model options employed are

summarized in Table 2.2-1 and are discussed further below.

Table 2.2-1 AERMOD version 09292 model options for RTR II modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly ambient concentration

Source type Point, area represented as pseudo point source

Receptor orientation Polar (13 rings and 16 radials) Discrete (census block centroids)

Terrain characterization Actual from USGS 1-degree DEM data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NW S from nearest site (158 stations)

In HEM-AERMOD, meteorological data are ordinarily select ed from a list of 158 National Weather Service (NWS) surface observation stat ions across the continental United States, Alaska, Hawaii, and Puerto Rico. In most cases, the nearest station is selected as representative of the conditions at the subject facility. Idea lly, when considering off-site meteorological data, most site specific dispersion modeling efforts will employ up to five years of data to capture variabil ity in weather patterns from year to year. However, because we had an insufficient number of appropriate ly formatted model input files derived from available meteorological data, we modeled onl y a single year, typica lly 1991. While the selection of a single year may result in under-prediction of long- term ambient levels at some locations, likewise it may result in over-predicti on at others. For each facility identified by its characteristic latitude and long itude coordinates, the closest meteorological station was used in the dispersion modeling. The average dist ance between a modele d facility and the applicable meteorological station was 40 miles (72 km). Appendix 3 (Meteorological Data Processing Using AERMET for HEM-AERMOD) provides a complete listing of stations and assumptions along with further details used in processing the data through AERMET. The sensitivity of model results to the selection of the nearest weather station and the use of one year of meteorological data is discussed in “Risk and Technology Review (RTR) Risk Assessment Methodologies” [ 2 ].

The HEM-AERMOD model estimates ambient concen trations at the geog raphic centroids of census blocks (using the 2000 Census), and at othe r receptor locations th at can be specified by

the user. The model accounts for the effect s of multiple facili ties when estimating concentration impacts at each block centroid. Typically, we combined only the impacts of facilities within the same source category, and assessed chronic exposur e and risk only for census blocks with at least one resident (i .e., locations where people may reasonably be7

assumed to reside rather than receptor points at the fenceline of a facility). Chronic ambient

concentrations were calculated as the annual average of all estimated short-term (one-hour)

concentrations at each block centroid. Possible future residential use of currently uninhabited areas was not considered. Census blocks, the fi nest resolution availa ble in the census data, are typically comprised of approximately 40 people or about ten households.

In contrast to the developmen t of ambient concentrations fo r evaluating long-term exposures, which was performed only for occupied census blocks, worst-case short-term (one-hour)

concentrations were estimated both at the censu s block centroids and at points nearer the facility that represent locations where people may be present for short periods, but generally no nearer than 100 meters from the center of the facility (note that for large facilities, this 100-meter ring could still contain locations inside the facility property). Since short-term emission rates were needed to screen for the potential for hazard via acute exposures, and since the NEI contains only annual emission tota ls, we generally apply the assumption to all source categories that the maxi mum one-hour emission rate from any source was ten times the average annual hourly emission rate for that s ource. For the wood furniture source category, we applied an hourly emission factor of 4(x) the annual average emission rate based upon an engineering review of all the em ission production processes for each pollutant. Rationale for

this acute emissions factor is document ed in a memo to the rulemaking docket [ 7 ].

The average hourly emissions rate is defined as the total emissions for a year divided by the total number of operating hours in the year. This choice of a factor of ten for screening was originally based on engineering judgment. To develop a more robust peak-to-mean emissions factor, and in response to one of the key messages from the SAB consultation on our RTR Assessment Plan, we performed an analysis us ing a short-term emi ssions dataset from a

number of sources located in Texa s (originally reported on by Allen et al. 2004)[ 8 ]. In that report, the Texas Environmental Research C onsortium Project compared hourly and annual emissions data for volatile organic compounds fo r all facilities in a heavily-industrialized 4- county area (Harris, Galveston, Chambers, and Brazoria Counties, TX) over an eleven-month time period in 2001. We obtained the dataset an d performed our own analysis, focusing that analysis on sources which reported emitting high quantities of HAP over short periods of time (see Appendix 4, “Analysis of data on short- term emission rates relative to long-term emission rates”). Most peak emission events were less than twice the annual average, and the highest was a factor of 11 times the annual average. Based on these results, we chose the factor of ten for all initial acute screening; it is intended to cover routinely-variable emissions as well as startup, shutdown, a nd malfunction (SSM) emissions. While there have been some documented emission excursions above this level, our analysis of the data from the Texas Environmental Research Consortium suggests that this factor should cover more than 99% of the short-term peak gaseous or volatile HAP emissions from typical industrial sources.

Census block elevations for HEM-AERMOD modeling were determined nationally from the US Geological Service 1-degree digital elevation model (DEM ) data files, which have a spatial resolution of about 90 meters. Elevations of polar grid points used in estimating shortand long-term ambient concentrations were assu med to be equal to the highest elevation of any census block falling within the polar grid sector corresponding to the grid point. If a

sector does not contain any blocks, the model defaults the elevation to that of the nearest8

block. If an elevation is not provided for the emission source, the model uses the average elevation of all sectors within the innermost model ring.

In addition to using receptor elevation to determine plume height, AERMOD adjusts the plume’s flow if nearby elevated hills are expect ed to influence the wind patterns. For details on how hill heights were estimated and used in the AERMOD modeling, see Appendix 2.



### 2.3 Estimating human in halation exposure

We used the annual average ambient air con centration of each HAP at each census block centroid as a surrogate for the lifetime inhalati on exposure concentration of all the people who

reside in the census block. That is, the risk analysis did not cons ider either the short-term or long-term behavior (mobility) of the exposed populations and its poten tial influence on their

exposure.

We did not address short-term human activity for two reasons. First, our experience with the 1996, 1999, 2002 and 2005 NATA assessments [ 9 ] (which modeled daily activity using EPA’s HAPEM model) suggests that, given our current unders tanding of microenvironment concentrations and daily activ ities, modeling short-term ac tivity would, on average, reduce risk estimates about 25% for particulate HAPs; it will also reduce risk estimates for gaseous HAPs, but typically by much less. Second, ba sing exposure estimates on average ambient

concentrations at census block centroids may underestimate or overestimate actual exposure

concentrations at some residences. Further reducing exposure estimates for the most highly exposed residents by modeling their short-term be havior could add a systematic low bias to

these results.

We did not address long-term migration nor population growth or decrease over 70 years, instead basing the assessment on the assumpti on that each person’s predicted exposure is

constant over the course of their lifetime which is assumed to be 70 years. In assessing cancer risk, we generally estimated three metrics; the maximum individual risk (MIR), which is

defined as the risk associated with a lifetime of exposure at the highe st concentration; the population risk distribution; a nd the cancer incidence. The assumption of not considering short or long-term population mobility does not bias the estimate of the theoretical MIR nor

does it affect the estimate of cancer incidence since the total populati on number remains the same. It does, however, affect the shape of the distribution of individual risks across the affected population, shifting it to ward higher estimated individua l risks at the upper end and reducing the number of people estimated to be at lower risks, thereby biasing the risk estimates high.

When screening for potentially significant acute exposures, we used an estimate of the highest hourly ambient concentration at any off-site location as the surrogate for the maximum potential acute exposure concentration for any individual.



### 2.4 Acute Risk Screening and Refined Assessments

In establishing a scientifically defensible approa ch for the assessment of potential health risks due to acute exposures to HAP, we followed the same general approach that is used for developing chronic health risk assessments under the residual ri sk program. That is, we9

developed a tiered, iterative approach. This a pproach to risk assessment was endorsed by the National Academy of Sciences in its 1993 pub lication “Science and Judgment in Risk Assessment” and subsequently was adopted in the EPA’s “Residual Risk Report to Congress”

in 1999.

The assessment methodology is designed to el iminate from further consideration those

facilities for which we have confidence that no acute adverse health effects of concern will occur. To do so, we use what is called a tier ed, iterative approach to the assessment. This means that we begin with a screening assessmen t, which relies on readily-available data and uses conservative assumptions that in combina tion approximate a worst-case exposure. The result of this screening process is that either the facility be ing assessed poses no acute health risks (i.e., it “screens out”), or that it requires further, more refined assessment. A refined assessment could use industry- or site-specific data on the temporal pa ttern of emissions, the layout of emission points at the facility, th e boundaries of the facil ity, and/or the local meteorology. In some cases, all of these site-s pecific data would be needed to refine the assessment; in others, lesser amounts of site-spe cific data could be used to determine that
acute exposures are not a con cern, and significant additional data collection would not be

necessary.

Acute health risk screening was performed as the first step. We used conservative assumptions for emission rates, meteorology, an d exposure location. We used the following worst-case exposure assumptions in our screening approach:

• Peak 1-hour emissions were assumed to equal 10 times the average 1-hour emission

rates.

• For facilities with multiple emission points, peak 1-hour emissions were assumed to occur at all emission points at the same time.

• For facilities with multiple emission points, 1-hour concentrations at each receptor

were assumed to be the sum of the maximum concentrations due to each emission point, regardless of whether those maximum concentrations occurred during the same

hour.

• Worst-case meteorology (from one year of local meteorology) was assumed to occur at the same time the peak emission rates occur. The recommended EPA local-scale dispersion model, AERMOD, was used fo r simulating atmospheric dispersion.

• A person was assumed to be located downwind at the point of maximum impact during this same 1-hour period, but no nearer to the source than 100 meters.

• The maximum impact was compared to multiple short-term health benchmarks for the chemical being assessed to determine if a possi ble acute health risk might exist. These

benchmarks are described in section 2.6 of this report.

As mentioned above, when we identify acu te impacts which exceed their relevant benchmarks, we pursue refining our acute screening estimates. In some cases, this includes use of a refined emissions multiplier to estimate the peak hourly emission rates from the average rates. In this case as mentioned earlier in section 2.2, our industry experts have advised us that a refined emissions multiplier of 4 should be used to cover routine variations in process emissions. We also conducted a re view of the layout of emission points at the10

facility with the facil ity boundaries to determine the maxi mum off-site acute impact for the

facilities that did not screen out during the initial model run. Refer to Appendix 7 for the

detailed results for these sites.



### 2.5 Multipathway and enviro nmental risk screening

The potential for significant hum an health risks due to expos ures via routes other than inhalation (i.e., multipathway exposures) was screened by first determining whether any sources emitted any hazardous air pollutants known to be persistent and bioaccumulative in the environment (PB-HAP). The PB-HAP co mpounds or compound classes are identified for the screening from the EPA’s Air Toxics Risk Assessment Library [ 10 ]. Examples of PBHAP are cadmium compounds, chlordane, chlo rinated dibenzodioxins and furans, DDE, heptachlor, hexachlorobenzene, hexachlo rocyclohexane, lead compounds, mercury compounds, methoxychlor, polychlor inated biphenyls, polycyclic organic matter (POM), toxaphene, and trifluralin. Emi ssions of 3 different PB HAP we re identified in the emissions inventory for the Wood Furniture Manufacturing source category; they are POM, lead, and

cadmium. These emissions were evaluated for potential non-inhalati on risks and adverse environmental impacts using our recently-devel oped screening scenario which was developed

for use with the TRIM.FaTE 2 model. This screening scenario uses environmental media outputs from the peer-reviewed TRIM.FaTE to estimate the maximum potential ingestion risks for any specified emission scenario by usi ng a generic farming/fish ing exposure scenario

that simulates a subsistence environment. The screening scenario retains many of the ingestion and scenario inputs developed for EP A’s Human Health Risk Assessment Protocols (HHRAP) for hazardous wast e combustion facilities. 3 In the development of the screening scenario a sensitivity analysis was conducted to ensure that its key design parameters were

established such that environmental media c oncentrations were not underestimated, and to also minimize the occurrence of false positives for human health endpoints. See Appendix 5 for a complete discussion of the development a nd testing of the screening scenario, as well as for the values of facility-level emission thresholds develope d for screenin g potentially significant multi-pathway impacts. For the purpose of developing emissions thresholds for our multi-pathway screening, we derived emissi on levels for each PB HAP at which the

maximum human health risk would be 1 in a million for lifetime cancer risk or a hazard quotient of 1.0 for noncancer impacts. In ev aluating the potential multi-pathway risks from emissions of lead compounds, rather than deve loping an emissions screening threshold, we compared maximum estimated chronic (3-month average) atmospheric concentrations with the current National Ambient Air Quality Stan dard (NAAQS) for lead. Values below the NAAQS were considered to have a low potentia l for multi-pathway risks of any significance.

Additionally, we evaluated the potential for significan t ecological exposures to non PB-HAP

from exceedances of chronic human health inhala tion thresholds in the ambient air near these

facilities. Human health dose -response threshold values are generally derived from studies conducted on laboratory animals (such as rodent s) and developed with the inclusions of uncertainty factors that could be as high as 3000. As a result, these human threshold values

2 EPA’s Total Risk Integrated Methodology (General Information) http://epa.gov/ttn/fera/trim_gen.html

3 EPA’s Human Health Risk Assessment Protocol (HHRAP) for Hazardous Waste Combustion Facilities; http://www.epa.gov/epaoswer/hazwaste/combust/riskvol.htm#volume111

are often significantly lower than the level expected to cause an adverse effect in an exposed

rodent. It should be noted that there is a scarcity of data on th e direct atmospheric impact of these HAPs on other receptors, such as plants, birds, and wildlife. Thus, if the maximum inhalation hazard in an ecosystem is below the level of concern for humans, we have generally concluded that mammalian receptors shoul d be at no risk of adverse effects due to inhalation exposures from non PB-HAP, and have assurance that other ecological receptors are also not at any significant ri sk from direct atmospheric imp act. In some isolated cases where we have data indicating po tential adverse impacts on plants, birds, or other wildlife due to the direct atmospheric impacts of specific HAP s, we note that as an uncertainty and, where possible, refine our analysis by comparing our modeled impacts to available threshold values

from the scientific literature.



### 2.6 Dose-Response Assessment

2.6.1 Sources of chronic dose-response information

Dose-response assessment information (car cinogenic and non-carcinogenic) for chronic exposure (either by inhalation or ingestion) for the HAPs reporte d in the emissions inventory

for Wood Furniture Manufacturi ng sources is based on the EPA Office of Air Quality Planning and Standards’ exis ting recommendations for HAPs, also used for NATA 1999, 2002, and 2005 [ 11 ]. This information has been obtained from various sources and prioritized according to (1) conceptual consis tency with EPA risk assessment guidelines and (2) level of peer review received. The priori tization process was aimed at incorporatin

our assessments the best available science wi th respect to dose-response information. T

recommendations are based on the follo wing sources, in order of priority: g into

he

1) US Environmental Protection Agency (EPA). EPA has developed dose-response assessments for chronic exposure for many of the pollutants in this study. These assessments typically provide a qualitative stat ement regarding the st rength of scientific data and specify a reference concentration (RfC, for inhalation) or reference dose (RfD, for ingestion) to protect against effects othe r than cancer and/or a unit risk estimate (URE, for inhalation) or slope factor (SF, for ingestion) to estimat e the probability of developing cancer. The RfC is defined as an “estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation e xposure to the human population (including sensitive subgroups) that is likely to be without an appreciabl e risk of deleterious effects during a lifetime.” The URE is defined as “the upper-bound excess ca ncer risk estimated

to result from continuous lifet ime exposure to an agent at a concentration of 1 μg/m 3 in air.” The SF is “an upper bound, approximating a 95% confidence limit, on the increased cancer risk from a lifetime exposure to an agen t. This estimate, [is] usually expressed in units of proportion (of a populat ion) affected per mg/kg-da y...” EPA disseminates doseresponse assessment information in several fo rms, based on the level of review. The Integrated Risk Information System (IRIS) [ 12 ] is an EPA database that contains

scientific health assessment information, including dose-response information. All IRIS assessments since 1996 have also undergone in dependent external peer review. The current IRIS process includes review by EPA sc ientists, interagency reviewers from o federal agencies, and the public, and peer revi ew by independent scie ntists external to EPA. EPA’s science policy approach, under th e current carcinogen guidelines, is to use ther12

linear low-dose extrapolation as a default option for carcinogens for which the mode of action (MOA) has not been iden tified. We expect future EPA dose-response assessme to identify nonlinear MOAs where appropriate, and we will use those analyses (once they are peer reviewed) in our risk assessments. At this time, however, there are no availab carcinogen dose-response assessments for i nhalation exposure that are based on a

nonlinear MOA

. nts

le

2) US Agency for Toxic Substances and Disease Registry (ATSDR). ATSDR, which is part of the US Department of Health and Huma n Services, develops and publishes Minimum Risk Levels (MRLs) [ 13 ] for inhalation and oral exposur e to many toxic substances. As stated on the ATSDR web site: “Following di scussions with scientists within the Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a practice similar to that of the EPA's Refere nce Dose (RfD) and Reference Concentration (RfC) for deriving substance specific health guidance levels for non neoplastic endpoints.” The MRL is defined as “an estimate of daily human exposure to a subs tance that is likely to be without an appreciable risk of advers e effects (other than cancer) over a specified duration of exposure.” ATSDR describes MRLs as substance-specific estimates to be used by health assessors to select environm ental contaminants for further evaluation. Exposures above an MRL do not necessarily re present a threat, and MRLs are therefore not intended for use as predictors of adverse health effects or for setting cleanup levels.

3) California Environmental Protection Agency (CalEPA). The CalEPA Office of

Environmental Health Hazard Assessment ha s developed dose-response assessments for many substances, based both on carcinogenicity a nd health effects other than cancer. The process for developing these assessments is si milar to that used by EPA to develop IRIS values and incorporates signifi cant external scientific peer review. As cited in the CalEPA Technical Support Document fo r developing their chronic assessments 4 : “The guidelines for developing chronic inhala tion exposure levels incorporate many recommendations of the U.S. EPA [ 14 ] and NAS [ 15 ].” The non-cancer information

includes available inhalation hea lth risk guidance values expr essed as chronic inhalation reference exposure levels (RELs) [ 16 ]. CalEPA defines the R EL as “the concentration

level at or below which no health effects are anticipated in the gene ral human population.” CalEPA's quantitative dose-response info rmation on carcinogenicity by inhalation exposure is expressed in terms of the URE [ 17 ], defined similarly to EPA's URE.

In developing chronic risk estimates, we adju sted dose-response values for some HAPs based on professional judgment, as follows:

1) In the case of HAP categories such as gl ycol ethers and cyanide compounds, the most conservative dose-response value of the chem ical category is used as a surrogate for other compounds in the group for which dose-response values are not available. This is done in order to examine, under conservative assumptions, whether these HAPs that lack

4 Air Toxics Hot Spots Program, Risk Assessment Guidelines, Part III - Technical Support Document

for the Determination of Non-cancer Chronic Referen ce Exposure Levels. Air Toxicology and Epidemiology Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency. February 2000 (http://www.oehha.ca.gov/air/chronic_rels/pdf/relsP32k.pdf)13

dose- response values may pose an unaccepta ble risk and require further examination, or

screen out from further assessment.

2) Where possible for emissions of unspecified mixtures of HAP categories such as metal compounds and POM, we apply category-specific chemical speciation profiles appropriate to the source category to de velop a composite dose-re sponse value for the
category.

3) In 2004, the EPA determined that the Chemi cal Industry Institute of Toxicology (CIIT) cancer dose-response value for formaldehyde (5.5 x 10 -9 per μ g/m 3 ) was based on better

science than the IRIS dose-response value (1.3 x 10 -5 per μ g/m 3 ), and we switched from using the IRIS value to the CIIT value in risk assessments supporti ng regulatory actions. Based on subsequent, published researc h, however EPA changed its determination regarding the CIIT m odel, and in 2010 EPA returned to using 1991 IRIS value. The National Academy of Sciences (NAS) completed its review of the EPA’s draft assessment in April of 2011 ( http://www.nap.edu/cata log.php?record_id=13142 ), and EPA has been working on revising the formaldehyde assessment. The EPA will follow the NAS Report recommendations and will present results obtained by implementing the biologically based dose response (BBDR) model for formaldehyde. The EPA will compare these estimates with those curren tly presented in the External Review draft of

the assessment and will discuss their stre ngths and weaknesses. As recommended by the NAS committee, appropriate sensitivity and uncertainty analyses will be an integral component of implementing the BBDR model. In the interim, we will present findings using the 1991 IRIS value as a primary estim ate, and may also consider other information

as the science evolves.

4) The IRIS URE for nickel inhalation was de rived from evidence of the carcinogenic effects of insoluble nickel compounds in crysta lline form. Soluble nick el species, and insoluble species in amorphous form, do not appe ar to produce genotoxic e ffects by the same toxic

mode of action as inso luble crystalline nickel. Nickel sp eciation information for some of the largest nickel-emitting sources (including oil combustion, coal combustion, and others) suggests that at least 35% of tota l nickel emissions may be soluble compounds. The remaining insoluble nickel emissions ar e not well-characterized. Consistent with this limited information, this assessment will conservatively assume that 65% of emitted

nickel is insoluble and that all insoluble nickel is crysta lline. The nickel URE (based on nickel subsulfide, and representative of pure insoluble crys talline nickel) was adjusted to reflect an assumption that 65% of th e total mass of nickel may be carcinogenic. The noncancer dose-response values were not adjusted because noncancer effects of nickel are not thought to be limite d to the crystalline, insoluble form

.

5) A substantial proportion of POM reported to EPA’s national emission inventory (NEI) is not speciated into individual compounds. As a result, it is necessary to apply the same simplifying assumptions to assessments that were used for the 1999 NATA study [ 18 ]. The NATA approach partitions POM into eight different non-overlapping “groups” that are modeled as separate pollutants. Each POM group comprises POM species of similar carcinogenic potency, for wh ich we can apply the same URE.14

The emissions inventory for the wood furnitu re manufacturing sour ce category includes emissions of 49 HAP with available quantitati ve chronic inhalation dose-response values. These HAP, their dose-response values, and the s ource of the values are listed in Tables 2.6-1 (a) through (c).

Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens

URE (unit risk estimate for cancer) 5 = cancer risk per μ g/m3 of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Health Hazard Assessment, EPA/OAQPS = interim value recommended by the EPA Office of Air Quality Planning and Standards.

Pollutant CAS

Number 6 URE 5
(1/ μ g/m3) Source

Acetaldehyde 75070 2.2E-06 IRIS
Acrylonitrile 107131 6.8E-05 IRIS
Arsenic compounds 174 4.3E-03 IRIS

Benzene 7 71432 7.8E-06 IRIS
Beryllium compounds 175 2.4E-03 IRIS
Benzyl chloride 100447 4.9E-05 CAL Bis(2-Ethylhexyl)phthalate 117817 2.4E-06 CAL
Cadmium compounds 7440439 1.8E-03 IRIS
p-Dichlorobenzene 106467 1.1E-05 CAL
1,4-Dioxane 123911 7.7E-06 CAL
Ethyl benzene 100414 2.5E-6 CAL
Formaldehyde 8 50000 1.3E-05 IRIS
Methylene chloride 75092 4.7E-07 IRIS
Naphthalene 91203 3.4E-05 CAL
Nickel compounds 186 4.8E-04 IRIS Polycyclic Organic Matter 246 9

- Benzo(a)anthracene 56553 1.1E-04 CAL - Benzo(a)pyrene 50328 1.1E-03 CAL - Benzo(b)fluoranthene 205992 1.1E-04 CAL - Benzo(k)fluoranthene 207089 1.1E-04 CAL

5 The URE is the upper-bound excess cancer risk estimated to result from continuous lifetime exposure to an agent at a concentration of 1 μ g/m 3 in air. URE’s are considered upper bound estimates meaning they represent a plausible upper limit to the true value.

6 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we have used a surrogate 3-digit identifier corresponding to the group’s position on the CAA list of HAPs.

7 The EPA IRIS assessment for benzene provides a range of plausible UREs. This assessment used the highest value in that range, 7.8E-06 per ug/m3. The low end of the range is 2.2E-06 per ug/m 3

. 8 The EPA has used the CITT URE value, 5.5x10 -9 per mg/m 3 to characterize formaldehyde cancer risk in some

Instances.

9 Assigned the URE associated with a mixture of POM compounds having a similar potency. Details of this method, also used in the 1999 National Air Toxics Assessment, are available at http://www.epa.gov/ttn/atw/nata1 999/99pdfs/pomapproachjan.pdf15

- Chrysene 218019 1.1E-05 CAL - Dibenzo(a,h)anthracene 53703 1.2E-03 CAL - Indeno(1,2,3-cd)pyrene 193395 1.1E-04 CAL Propylene Oxide 75569 3.7E-6 IRIS
2,4-Toluene Diisocyanate 26471625 1.1E-05 CAL
Trichloroethylene 79016 2.0E-06 CAL Vinyl Chloride 75014 8.8E-06 IRIS

Table 2.6-1 (b) Dose-Response Values for Chronic Oral Exposure to Carcinogens

SF (oral slope factor for cancer) = cancer risk per mg /kg/d of average lifetime expos ure. Sources: IRIS = EPA Integrated Risk Information Sy stem, CAL = California EPA Office of Envi ronmental Health Hazard Assessment, EPA/OAQPS = interim value recommended by the EPA Office of Air Quality Planning and Standards.

Pollutant CAS Number 10 SF (1/mg/kg/d) Source Polycyclic organic matter (POM) 246 0.5 - Benzo(a)anthracene 56553 1 CAL - Benzo(a)pyrene 50328 7 CAL - Benzo(b)fluoranthene 205992 1 CAL - Benzo(k)fluoranthene 207089 1 CAL - Chrysene 218019 0.1 CAL - Dibenz(a,h)anthracene 53703 4 CAL
- Indeno(1,2,3-cd)pyrene 193395 1 CAL

Table 2.6-1 (c) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to t he human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleter ious effects during a lifet ime. Sources: IRIS = EPA Integrated Risk Informatio n System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = Agency for Toxi c Substances Disease Registry.

Pollutant CAS Numbe r 6 RfC (mg/m3) Source 10

Acetaldehyde 7507 0 0.009 IRIS -- L
Acrylonitrile 10713 1 0.002 IRIS - M
Acrylic acid 7910 7 0.001 IRIS - M
Arsenic compounds 174 0.000015 CAL

Benzene 7143 2 0.03 IRIS – M
Cadmium compounds 176 0.00001 ATSDR

Chlorobenzene 10890 7 1 CAL Cresols (mixed) 11 9548 7 0.6 CAL Cyanide compounds 12 180 0.003 IRIS - H

10 The descriptors L (low), M (medium), and H (high) have been added for IRIS RfC values to indicate the overall level of confidence in the RfC value, as reported in the IRIS file.

11 Cresols (mixed) includes all isomers (ortho, meta, and para)

12 The value for hydrogen cyanide was used as a surrogate for all cyanide compounds16

Table 2.6-1 (c) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to t he human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleter ious effects during a lifet ime. Sources: IRIS = EPA Integrated Risk Informatio n System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = Agency for Toxi c Substances Disease Registry.

Pollutant CAS Number 6 RfC (mg/m3) Source 10

p-Dichlorobenzene 10646 7 0.8 IRIS – M

Diethanolamine 11142 2 0.003 CAL
1,4-Dioxane 12391 1 3.6 ATSDR
Ethyl benzene 10041 4 1 IRIS – L
Ethylene glycol 10721 1 0.4 CAL
Formaldehyde 13 5000 0 0.0098 ATSDR Glycol ethers 14 181 0.02 IRIS – M - Diethylene glycol monobutyl ether (DGBE) * 11234 5 0.02 - Diethylene glycol monomethyl ether (DGME) * 11177 3 0.02
- Ethyl glycol ethyl ether 11080 5 0.2 IRIS - M - Ethylene glycol methyl ether (EGME) 10986 4 0.02 IRIS - M - 2-Butoxyethyl acetate * 11207 2 0.02
- Ethylene glycol ethyl ether acetate 11115 9 0.3 CAL - Butyl carbitol acetate * 12417 4 0.02 - Triethylene glycol * 11227 6 0.02 - Diethylene Glycol Monoethyl Ether * 111-90-0 0.02 - Phenyl Cellosolve * 12299 6 0.02 - Propyl Cellosolve * 280730 9 0.02
Hexamethylene-1,6-diisocyanate 82206 0 0.00001 IRIS - L

n-Hexane 11054 3 0.7 IRIS – M
Hydrochloric acid 764701 0 0.02 IRIS – L
Hydrogen cyanide 7490 8 0.003 IRIS - L
Lead and compounds 182 0.00015 EPA OAQPS
Maleic anhydride 10831 6 0.0007 CAL
Manganese compounds 183 0.00005 IRIS - H

Methanol 6756 1 4 CAL
4,4’-Methylenediphenyl diisocyanate 10168 6 0.0006 IRIS - M
Methyl isobutyl ketone 10810 1 3 IRIS – L/M
Methylene chloride 7509 2 1 ATSDR

Methyl methacrylate 8062 6 0.7 IRIS - L
Naphthalene 9120 3 0.003 IRIS – M
Nickel compounds 186 0.00009 ATSDR

Phenol 10895 2 0.2 CAL
Propylene oxide 7556 9 0.03 IRIS - M

14 The RfC value for ethylene glycol methyl ether was used as a surrogate for all glycol ethers without an RfC; (denoted with an * ).17

Table 2.6-1 (c) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to t he human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleter ious effects during a lifet ime. Sources: IRIS = EPA Integrated Risk Informatio n System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = Agency for Toxi c Substances Disease Registry.

Pollutant CAS Number 6 RfC (mg/m3) Source 10

Selenium compounds 778249 2 0.02 CAL Styrene 10042 5 1 IRIS – M

Toluene 10888 3 5 IRIS – H
2,4-Toluene diisocyanate 2647162 5 0.00007 IRIS - M
1,1,1-Trichloroethane 7155 6 5 IRIS - M
Trichloroethylene 7901 6 0.6 CAL
Triethylamine 12144 8 0.007 IRIS - L
Vinyl acetate 10805 4 0.2 IRIS – H
Vinyl chloride 7501 4 0.1 IRIS - H Xylenes (mixed) 133020 7 0.1 IRIS – M
m-Xylene 15 10838 3 0.1 IRIS – M
o-Xylene 13 9547 6 0.1 IRIS – M
p-Xylene 13 10642 3 0.1 IRIS – M

2.6.2 Sources of acute dose-response information
Hazard identification and dose-response assessment information for the preliminary acute inhalation exposure assessments are based on the existing recommenda tions of OAQPS for HAPs [ 19 ]. Depending on availability, the result s from screening acute assessments are compared to both “no effects” reference levels for the general public, such as the California Reference Exposure Levels (RELs), as well as emergency response levels, such as Acute Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines (ERPGs), with the recognition that the ultimate interpretation of any potential risks associated with an estimated exceedance of a particular refe rence level depends on the definition of that level and any limitations expressed therei n. Comparisons among different available

inhalation health effect refere nce values (both acute and chro nic) for selected HAPs can be found in a newly released EPA document (Ref erence: U.S. EPA. Graphical Arrays of Chemical-Specific Health Effect Reference Valu es for Inhalation Exposures [Final Report]. U.S. Environmental Protection Agen cy, Washington, D.C., EPA/600/R-09/061, 2009. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003.)

California Acute Reference Exposure Levels (RELs ). The California Environmental Protection Agency (CalEPA) has developed acu te dose-response refe rence values for many substances, expressing the results as acute i nhalation Reference Exposure Levels (RELs).

15 The RfC for mixed xylene was used as a surrogate.18

The acute REL ( http://www.oehha.ca.gov/air/pdf/acuterel.pdf ) is defined by CalEPA as

“the concentration level at or below which no adverse health effects are anticipated for a specified exposure duration. [ 20 ]. RELs are based on the most sensitive, relevant, adverse health effect reported in the medi cal and toxicological literature. RELs are designed to protect the most sensitive individuals in the population by the inclusion of margins of safety. Since margins of safety are incorporated to address data gaps and uncertainties, exceeding the REL does not auto matically indicate an adverse health impact.” Acute RELs are developed fo r 1-hour (and 8-hour) exposures. The values incorporate uncertainty factors similar to those used in deriving EPA’s Inhalation Reference Concentrations (RfCs) for chronic exposures (and, in fact, California also has developed chronic RELs).

Acute Exposure Guideline Levels (AEGLs ). AEGLs are developed by the National Advisory Committee on Acute Exposure Guideline Levels (NAC/AEGL) for Hazardous Substances, and then reviewed and published by the Natio nal Research Council As described in the Committee’s “Standing Operating Procedures (SOP)” ( http://www.epa.gov/op ptintr/aegl/pubs/sop.pdf ), AEGLs “represent threshold exposure limits for the general public and are applicable to emer gency exposures ranging from 10 min to 8 h.” Their intended application is “for conducting risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real time emergency response actions, for accidental chemical releases at fixe d facilities and from tran sport carriers.” The document states that “the primary purpos e of the AEGL program and the NAC/AEGL Committee is to develop guideline levels for once-in-a-lifetim e, short-term exposures to

airborne concentrations of acute ly toxic, high-priority chemi cals.” In detailing the intended application of AEGL values, the document states that “It is antic ipated that the AEGL values will be used for regulatory and nonregulatory purposes by U.S. Federal and State agencies, and possibly the international community in c onjunction with chemical emergency response, planning, and prevention programs. More spec ifically, the AEGL values will be used for conducting various risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real-time emerge ncy response actions, for accidental chemical releases at fixed facilities and from transpor t carriers.” The NAC/A EGL defines AEGL-1 and

AEGL-2 as:

“AEGL-1 is the airborne concentr ation (expressed as ppm or mg/m 3 ) of a substance above which it is predicted that th e general population, including su sceptible individuals, could experience notable discomfort, irritation, or certain asymptomatic nonsensory effects. However, the effects are not disabling and ar e transient and revers ible upon cessation of exposure.”

“AEGL-2 is the airborne concentr ation (expressed as ppm or mg/m 3 ) of a substance above which it is predicted that th e general population, including su sceptible individuals, could experience irreversible or other serious, long-lasting adverse h ealth effects or an impaired ability to escape.”

“Airborne concentrations below AEGL-1 repr esent exposure levels that can produce mild and progressively increasing but transient and nondisabli ng odor, taste, and sensory19

irritation or certain asymptomatic, nonsenso ry effects. With increasing airborne concentrations above each AEGL, there is a progressive increase in the likelihood of occurrence and the severity of effects described for each corresponding AEGL. Although the AEGL values represent threshold levels for the general public, including susceptible subpopulations, such as infants, children, the el derly, persons with asthma, and those with other illnesses, it is recognized that indivi duals, subject to unique or idiosyncratic responses, could experience the effects de scribed at concentr ations below the corresponding AEGL.”

Emergency Response Planning Guidelines (ERPGs). The American Industrial Hygiene Association (AIHA) has developed Emergenc y Response Planning Guidelines (ERPGs) [ 21 ] for acute exposures at three different levels of severity. Thes e guidelines represent concentrations for exposure of the general population (but not pa rticularly sensitive persons) for up to 1 hour associated with effects expected to be mild or transient (ERPG-1), irreversible or serious (ERPG-2), and potentially life-threatening (ERPG-3).

ERPG values ( http://www.aiha.org/1documents /Committees/ERP-erpglevels.pdf ) are described in their supporting documentation as follows: “Emergency Response Planning Guidelines (ERPGs) were developed for emerge ncy planning and are intended as health based guideline concentrations for single exposures to chemicals. These gui delines (i.e., the ERPG Documents and ERPG values) are intended fo r use as planning tools for assessing the adequacy of accident prevention and emergenc y response plans, including transportation emergency planning and for developing community emergency response plans. The emphasis is on ERPGs as planning values: When an actua l chemical emergency occurs there is seldom

time to measure airborne concentrations and then to take action.” ERPG-1 and ERPG-2 values are defined by AIHA as follows:

“ERPG-1 is the maximum airborne concentration below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing other than mild

transient adverse health eff ects or without perceiving a clearly defined, objectionable

odor.”

“ERPG-2 is the maximum airborne concentration below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing or developing

irreversible or other serious h ealth effects or symptoms which could impair an individual's ability to take protective action.”

The emissions inventory for the wood furnitu re manufacturing sour ce category includes

emissions of 53 HAP with relevant and availa ble quantitative acute dose-response threshold values. These HAPs, their acute threshold values , and the source of the value are listed below

in Table 2.6-2.20

Table 2.6-2 Dose-Response Values for Acute Exposure

Pollutant CAS Number AEGL-1 (1-hr) (mg/m 3 ) AEGL-2 (1-hr) (mg/m 3 ) ERPG1 (mg/m 3 ) ERPG2 (mg/m 3 ) REL
1,1,1-Trichloroethane 71556 1300 3300 1900 3800 68
1,2-Epoxybutane 106887 210 410

1,4-Dioxane 123911 61 1200 3
2,4-Toluene diisocyanate 584849 0.14 0.59 0.071 1.1
4,4’-Meth y lenediphen y l diisocyanate 101688 5

Acetaldehyde 75070 81 490 18 360 0.47
Acrylic acid 79107 4.4 140 3 150 6
Acrylonitrile 107131 10 130 22 77
Arsenic compounds 7440382 0.0002
Benzyl chloride 100447 5.2 52 0.24

Benzene 71432 170 2600 160 480 1.3
Beryllium compounds 7440417 0.025

Chlorobenzene 108907 46 690

Cumene 98828 250 1500
Cyanide compounds 57125 2.2 7.8 11
Ethyl acrylate 140885 34 150 0.041 120
Ethyl Benzene 100414 140 4800
Formaldehyde 50000 1.1 17 1.2 12 0.055
Glycol ethers 16

Eth y lene g l y col meth y l
ether 109864 0.093

- 2- butoxyethyl acetate * 112072 0.093
- Butyl carbitol acetate * 124174 0.093

- Dieth y lene g l y col monobut y l
ether * 112345 0.093

- Dieth y lene g l y col monoeth y l
ether * 111900 0.093

- Dieth y lene g l y col monomethyl ether * 111773 0.093

- Eth y l Gl y col 2- ethoxyethanol 110805 0.37

- Eth y lene g l y col eth y l ether

acetate 111159 0.14

- Triethylene glycol * 112276 0.093
- Phenyl cellosolve * 122996 0.093
- Propyl cellosolve * 2807309 0.093

Hexane 110543 12000
Hydrochloric acid 7647010 2.7 33 4.5 30 2.1
Hydrogen cyanide 74908 2.2 7.8 11 0.34

16 The acute REL for ethylene glycol methyl ether (EGME) wa s used as a surrogate for all glycol ethers without an acute REL; (denoted with an * ).21

Table 2.6-2 Dose-Response Values for Acute Exposure

Pollutant CAS Number AEGL-1 (1-hr) (mg/m 3 ) AEGL-2 (1-hr) (mg/m 3 ) ERPG1 (mg/m 3 ) ERPG2 (mg/m 3 ) REL
Maleic anhydride 108316 0.8 8

Methanol 67561 690 2700 260 1300 28
Methyl methacrylate 80626 70 490
Methylene chloride 75092 690 1900 1000 2600 14
Nickel compounds 7440020 0.006

Phenol 108952 58 89 38 190 5.8
Propylene oxide 75569 170 690 120 590 3.1
Styrene 100425 85 550 210 1100 21

Toluene 108883 750 4500 190 1100 37
Trichloroethylene 79016 700 2400 540 2700
Triethylamine 121448 2.8
Vinyl acetate 108054 24 630 18 260
Vinyl chloride 75014 640 3100 1300 13000 180
Xylenes (mixed) 1330207 560 4000 22
- m-Xylene 108383 22
- p-Xylene 106423 22



### 2.7 Risk Characterization

2.7.1 General

The final product of the risk as sessment is the risk characteri zation, in which the information from the previous steps is integrated and an overa ll conclusion about risk is synthesized that is complete, informative, and useful for decision ma kers. In general, the nature of this risk characterization depends on the in formation available, the application of the risk information and the resources available. In all cases, majo r issues associated with determining the nature

and extent of the risk are identified and disc ussed. Further, the EPA Administrator’s March

1995 Policy for Risk Characterization [ 22 ] specifies that a risk ch aracterization “be prepared in a manner that is clear, transparent, r easonable, and consistent with other risk characterizations of similar scope prepared acro ss programs in the Agency.” These principles of transparency and consistency have been reinforced by the Agency’s Risk Characterization

Handbook [ 23 ], in 2002 by the Agency’s information quality guidelines [ 24 ], and in the OMB/OSTP September 2007 Memorandum on U pdated Principles for Risk Analysis 17 , and are incorporated in these assessments.

Estimates of health risk are pres ented in the context of uncertainties and limitations in the data

17 Memorandum for the Heads of Executive Departments and Agencies - Updated Principles for Risk Analysis (September 19, 2007), From Susan E. Dudley, Administrator, Office of Information and Regulatory Affairs, Office of Management and Budget; and Sharon L. Hays, Associate Director and Depu ty Director for Science, Office of Science and Technology Policy (http://www.whitehouse.gov/omb/memoranda/fy2007/m07-24.pdf)22

and methodology. Through our tiered, iterative an alytical approach, we have attempted to reduce both uncertainty and bias to the greate st degree possible in these assessments, within

the limitations of available time and resources . We provide summari es of risk metrics (including maximum individual cancer risks and non-cancer hazards, as well as cancer incidence estimates) along with a discussion of the major uncertainties associated with their derivation to provide decision ma kers with the fullest pictur e of the assessment and its

limitations.

For each carcinogenic HAP included in an assess ment that has a potency estimate available, individual and population cancer risks were calculated by multiplying the corresponding lifetime average exposure estimate by the approp riate URE. This calculated cancer risk is defined as the upper-bound probability of de veloping cancer over a 70- yr period (i.e., the assumed human lifespan) at that exposure. Because UREs for most HAPs are upper-bound estimates, actual risks at a given exposure le vel may be lower than predicted, and could be

zero.

For EPA’s list of carcinogenic HAPs th at act by a mutagenic mode-of-action [ 25 ], we applied

EPA’s Supplemental Guidance for Asse ssing Susceptibility from Early-Life Exposure to Carcinogens [ 26 ]. This guidance has the effect of adjusting the URE by factors of 10 (for children aged 0-1), 3 (for children aged 2-15), or 1.6 (for 70 years of exposure beginning at birth), as needed in risk assessments. In th is case, this has the effect of increasing the estimated life time risks for these pollutants by a factor of 1.6. In addition, although only a

small fraction of the total POM emissions may be reported as individual compounds, EPA expresses carcinogenic potency for compounds in this group in term s of benzo[a]pyrene equivalence, based on evidence that carcinogenic POM have the same mutagenic mechanism of action as does benzo[a]pyrene. For this reason, EPA implementation policy [ 27 ] recommends applying the Supplemental Guidance to all carcinogenic PAHs for which risk estimates are based on relative potency. Acco rdingly, we applied the Supplemental Guidance to all unspeciated POM mixtures.

Increased cancer incidence for the entire receptor population with in the area of analysis was estimated by multiplying the estimated lifetime cancer risk for each census block by the number of people residing in that block, then summing the results for the entire modeled domain. This lifetime populati on incidence estimate was divide d by 70 years to obtain an estimate of the number of cancer cases per year.

In the case of benzene, the high end of th e reported cancer URE range was used in our assessments to provide a conservative estimate of potential cancer risks. Use of the high end of the range provides risk estimates that are approximately 3.5 times higher than use of the equally-plausible low end value. If the esti mated benzene–associated risks exceed 1 in a million, we also evaluated the impact of using the low end of the URE range on our risk

results.

Unlike linear dose-response assessments for can cer, noncancer health hazards generally are not expressed as a probability of an adverse occurrence. Inst ead, “risk” for noncancer effects is expressed by comparing an exposure to a refe rence level as a ratio. The “hazard quotient”23

(HQ) is the estimated exposure divided by a re ference level (e.g., the RfC). For a given HAP, exposures at or below the reference level ( HQ<1) are not likely to cause adverse health effects. As exposures increase above the reference level (HQs increasingly greater than 1), the potential for adverse effects increases. For exposures predicted to be above the RfC, the

risk characterization in cludes the degree of confidence ascribed to the RfC values for the compound(s) of concern (i.e., high, medium, or lo w confidence) and discusses the impact of this on possible hea lth interpretations.

The risk characterization for chronic effects othe r than cancer is expressed in terms of the HQ for inhalation, calculated for each HAP at each census block centroid. As discussed above, RfCs incorporate generally conservative uncer tainty factors in the face of uncertain extrapolations, such that an HQ greater than one does not necessarily suggest the onset of adverse effects. The HQ cannot be translated to a probability that adve rse effects will occur, and is unlikely to be proportional to advers e health effect outcomes in a population.

Screening for potentially significan t acute inhalation exposures also follows the HQ approach. We divided the maximum estimated acute expo sure by each available short-term threshold value to develop an array of HQ values relative to the various acute endp oints and thresholds. In general, when none of these HQ values is gr eater than one, there is no potential for acute risk. In those cases where HQ values above one are seen, additional information is used to determine if there is a potential for significant acute risks.

2.7.2 Mixtures
Since most or all receptors in these assessments receive exposures to multiple pollutants rather than a single pollutant, we estimated the aggregate health risks a ssociated with all the exposures from a particular source category combined.

To combine risks across multiple carcinogens, ou r assessments use the mixtures guidelines’ [ 28

, 29 ] default assumption of additivity of effects, and combine risks by summing them using the independence formula in the mixtures guidelines.

In assessing non-cancer hazard from chronic expo sures, in cases where different pollutants cause adverse health effects via completely different modes of action, it may be inappropriate to aggregate HQs. In consideration of th ese mode-of-action differences, the mixtures guidelines support aggregating effects of different substances in specif ic and limited ways. To conform to these guidelines, we aggregated non-cancer HQs of HAPs that act by similar toxic modes of action, or (where this information is absent) that affect the same target organ. This process creates, for each target organ, a target-organ-specific hazard index (TOSHI), defined as the sum of hazard quotients for indi vidual HAPs that affect the same organ or organ system. All TOSHI calcu lations presented here were based exclusively on effects occurring at the “critica l dose” (i.e., the lowest dose that pr oduces adverse health effects). Although HQs associated with some pollutants ha ve been aggregated into more than one TOSHI, this is done only in cases where the criti cal dose affects more than one target organ. Because impacts on organs or systems that o ccur above the critical dose have not been included in the TOSHI calculations, some TOSHIs may be underestimated. As with the HQ, the TOSHI should not be interprete d as a probability of adverse e ffects, or as strict delineation24

of “safe” and “unsafe” levels. Rather, the TO SHI is another measur e of the potential for adverse health outcomes associated with pollu tant exposure, and needs to be interpreted carefully by health scientists and risk managers.

Because of the conservative nature of the acute inhalation screening and th e variable nature of emissions and potential exposures, acute impact s were screened on an individual pollutant basis, not using the TOSHI approach.

2.7.3 Facility-wide Risks
To help place the source category risks in cont ext, we examined “facility-wide” risks using

the results of the 2005 NATA. For the facilities in th ese source categories, we extracted from the NATA results, based on facility ID, estim ates of the maximum inhalation cancer and

chronic non-cancer risks associated with all HAP emissions sources at the facility, including emissions sources that are not part of the s ource categories but that are located within a contiguous area and are under comm on control. We analyzed ri sks due to the inhalation of HAP for the populations residing within 50 kilomete rs of each facility. The results of these facility-wide assessments are summarized below in the Risk Characterization section of this document. The complete results of the facility -wide assessments are provided in Table 2 of Appendix 5.



### 3 Risk Results for the Wood Furnitur e Manufacturing Source Category

3.1 Source Category Descriptions and Results

The wood furniture manufacturi ng source category includes f acilities engaged in the

manufacture of wood furniture or wood furniture components. The industry encompasses the application of any protective, decorative, or functional coating to wood furniture or wood furniture components. This source category in cludes wood kitchen cabinets, wood residential furniture, upholstered residential and office furniture, wood office furniture and fixtures, partitions, shelving, lockers, and other wood fu rniture not included in one of the above categories. Finishing, gluing, cleaning, and washoff operations are the four processes that take place during wood furniture manufacturing that result in HAP emissions and are covered in the MACT rule for this category. A co mplete description of the wood furniture manufacturing source category can be f ound in the text of the NPRM.

We currently estimate that there are 405 wood furn iture facilities operating in the U.S. The NPRM data set at proposal contained 385 f acilities identified with a wood furniture manufacturing MACT code based upon the 2005 National-Scale Air Toxics Assessment (NATA) NEI. This was later changed to 384 f acilities after we receiv ed comments from the proposal that one facility was not within the wood furniture MACT source category. This one facility listed also had the highe st cancer risk for the source ca tegory. All references to this facility will be amended as well as any docum ents, attachments, and summaries concerning emissions and/or risk for this facility will be removed. While the NEI identifies 3 of these 384

facilities as area sources of HA P (and 381 as major sources), the uncertainty associated with these major and area source classifi cations is considered signific ant. Without verification of the area source status of individual facilities, EPA will consider all facilities in the NEI to be major sources in this and any future RTR risk assessments. Two of these three facilities were25

major sources at the MACT compliance date and the third is a synthetic minor facility. All three facilities are treated as majo r sources in this assessment.

The emissions for the wood furniture manufact uring source category NPRM data set (of 384 facilities) are summarized in Table 3.1-1. The total HAP emissions for the source category are approximately 5,600 tons per year. Based on these data, the HAP emitted in largest quantities by the source category are: tolu ene, xylenes (mixed), 1,1,1-trichloroethane, methanol, ethyl benzene, methyl isobutyl ketone, hexane, met hylene chloride, styrene, propyl cellosolve, diethylene glycol monobutyl ether, formaldehyde, and o-xylene. Emissions of these 13 HAP make up 99 percent of the total emissions by mass. Toluene was emitted in the largest quantity and is reported from 314 of the 384 facilities in the data set (82 percent).

To estimate chronic and acute HQ values for 66 f acilities reporting un-speciated emissions of glycol ethers, a speciation profile was develo ped based upon average sp eciation profiles from the 26 facilities that provided speciated emissions data. Rationale for this speciation profile is documented in a memo to the docket [ 30 ].

Persistent and bioaccumulative HAP (PB HAP) 18 emissions reported in the data set for the wood furniture source category include lea d, cadmium, and polycyclic organic matter.

Table 3.1-1 Summary of Emission s from the Wood Furniture Manu facturing Source Category and Availability of Dose-Response Values

HAP a Emissions (tpy) Number of

Facilities Reporting HAP (384 facilities in NPRM data set) Prioritized Inhalation Dose-Response Value Identified by OAQPS b

PBHAP? Unit Risk

Estimate

for Cancer? Reference

Concentration

for

Noncancer? Health

Benchmark

Values for Acute

Noncancer?

Toluene 1,840 314 Υ Υ Xylenes (Mixture) 1,623 283 Υ Υ
1,1,1-Trichloroethane 692 44 Υ Υ

Methanol 544 211 Υ Υ Ethyl Benzene 236 184 Υ Υ Methyl Isobutyl Ketone 222 158 Υ

Hexane 119 41 Υ Υ Methylene Chloride 72 20 Υ Υ Υ Styrene 51 23 Υ Υ Glycol Ethers
Propyl Cellosolve 46 73 (S) (S) Diethylene Glycol Monobutyl Ether 20 73 (S) (S)

Diethylene Glycol Monomethyl Ether 4 68 (S) (S)

Butyl Carbitol Acetate 2 64 (S) (S)

18 Persistent and bioaccumulative HAP are defined in the EPA’s Air Toxics Risk Assessment Library , Volume 1, EPA-453K-04-001A, wh ich is accessible from the EPA’s Technolo gy Transfer Network website for Fate, Exposure, and Risk Assessment at http://www.epa.gov/ttn/fera/risk_atra_vol1.html

.26

Table 3.1-1 Summary of Emission s from the Wood Furniture Manu facturing Source Category and Availability of Dose-Response Values

HAP a Emissions (tpy) Number of

Facilities Reporting HAP (384 facilities in NPRM data set) Prioritized Inhalation Dose-Response Value Identified by OAQPS b

PBHAP? Unit Risk

Estimate

for Cancer? Reference

Concentration

for

Noncancer? Health

Benchmark

Values for Acute

Noncancer?

Cellosolve Solvent 2 70 Y Y Ethylene Glycol Monoethyl Ether

Acetate 1 68 Y Y

2-Butoxyethyl Acetate 0.6 67 (S) (S) Diethylene Glycol Monoethyl Ether 0.4 67 (S) (S)

Phenyl Cellosolve 0.2 66 (S) (S)
Triethylene glycol 0.1 65 (S) (S)
Formaldehyde 20 140 Υ Υ Υ o-Xylene 14 9 Υ Υ Methyl Methacrylate 12 7 Υ Υ Ethylene Glycol 10 35 Υ Bis(2-Ethylhexyl)Phthalate 10 28 Υ Υ

Cumene 9 84 Υ Υ Dibutyl Phthalate 4 43
Ethyl Acrylate 4 1 Υ Vinyl Acetate 3 42 Υ Υ Naphthalene 2 55 Υ Υ m-Xylene 2 8 Υ Υ

Chlorobenzene 1 1 Υ Υ

Phenol 0.9 15 Υ Υ 4,4'-Methylenediphenyl Diisocyanate 0.8 10 Υ Υ

Acrylic Acid 0.8 1 Υ Υ Dimethyl Phthalate 0.7 8
Hydroquinone 0.6 5 Cyanide & Compounds 0.6 2 Υ p-Xylene 0.5 6 Υ Υ p-Dioxane 0.4 5 Υ Υ Υ Manganese Compounds
Manganese & Compounds 0.4 50 Υ

Manganese Trioxide 0.03 8 Υ Lead Compounds 0.4 3 c Υ Chromium Compounds

Chromium III 0.2 36

Chromium VI 0 29 Υ Υ Trichloroethylene 0.1 2 Υ Υ Υ Propylene Oxide 0.1 1 Υ Υ Υ Acetaldehyde 0.08 9 Υ Υ Υ27

Table 3.1-1 Summary of Emission s from the Wood Furniture Manu facturing Source Category and Availability of Dose-Response Values

HAP a Emissions (tpy) Number of

Facilities Reporting HAP (384 facilities in NPRM data set) Prioritized Inhalation Dose-Response Value Identified by OAQPS b

PBHAP? Unit Risk

Estimate

for Cancer? Reference

Concentration

for

Noncancer? Health

Benchmark

Values for Acute

Noncancer? 1,2-Epoxybutane 0.06 1 Υ

Benzene 0.05 7 Υ Υ Υ Cobalt Compounds 0.04 42 Υ 2,4-Toluene Diisocyanate 0.04 1 Υ Υ Υ Triethylamine 0.04 6 Υ Υ Hexamethylene Diisocyanate 0.02 6 Υ

Acrylonitrile 0.02 1 Υ Υ Υ Nickel Compounds 0.01 3 Υ Υ Υ Hydrochloric Acid 0.01 1 Υ Υ

Diethanolamine 0.006 4 Υ Hydrogen Cyanide 0.003 1 Υ Υ Quinone 0.001 1

o-Cresol 0.0009 1 Υ Maleic Anhydride 0.00009 1 Υ Υ Vinyl Chloride 0.000008 1 Υ Υ Υ Benzyl Chloride 0.000004 5 Υ Υ p-Dichlorobenzene 0.000003 1 Υ Υ Cadmium compounds 0.000003 1 Υ Υ Υ Selenium & Compounds 0.0000003 3 Υ Arsenic & Compounds (Inorganic Including Arsine) 0.0000006 1 Υ Υ Υ

POM 0.000000295 3 Υ Y Beryllium compounds 0.00000003 1 Υ Υ Υ

a Notes for how HAP were speciated for risk assessment:

• (S) – denotes a surrogate health benchmark value was used for estimating cancer or non-cancer risks

• For most metals, emissions reported as the elemental metal are combined with metal compound emissions (e.g.,

“cadmium” emissions modeled as “cadmium & compounds”). In the absence of speciation in formation, we assume the reported mass is 100 percent metal.

• For metal and cyanide compounds consisting of more than just the metal or cyanide ion (on which the dose-response values are based), emissions were adjusted according to the mass fraction of the metal or cyanide. For example, for lead nitrate, emissions were multip lied by 0.6218 to account for the mass of the one Pb atom (mass of 207.2) in a single molecule of Pb(HNO 3 ) 2 (total mass of 333.22). For metal compounds cont aining two metal species of interest, the NEI chemical was multiplied by adjustment factors two times (t o account for each metal of interest), resulting in two chemicals for modeling (i.e., toxicity to both metal compounds was assessed). For example, emissions of lead arsenate (PbAsH 3 O 4 ) were multiplied by 0.5935 to account for the mass of lead in the emitted compound, and were multiplied by 0.2146 to account for the mass of arsenic in the emitted compound.

• For emissions reported generically as “Glycol Ethers” or specific glycol ethers not found on EPA’s Technology Transfer Network for air toxics (see f ootnote b), emissions were treated as ethylene glycol methyl ether for the chronic noncancer risk assessment and ethylene glycol methyl ether for the acute noncan cer risk assessment.

• For emissions of any chemicals or chemical groups classified as polycyclic organic matter (POM), emissions were grouped into POM subgroups as found on EPA’s Technology Tr ansfer Network website for the 2002 National-Scale Air

Toxics Assessment at http://www.epa.gov/nata2002/methods.html#pom . ( Approach to Modeling POM ).28

b Specific dose-response values for each chemical are identified on EPA’s Technology Transfer Network website for air

toxics at http://www.epa.gov/ttn/atw/toxsource/summary.html and are also available in Appendix 5 of the Development of Preliminary Risk Estimat es for Source Categories Previously Subj ected to Technology-based Standards document.

c There is no reference concentration for lead. In considering noncancer hazards for lead in this preliminary assessment, we compared maximum 3-month ambient expos ure estimates to the Nati onal Ambient Air Quality St andards (NAAQS) for lead (0.15 μg/m 3 ). These NAAQS for lead were recently revi ewed with revisions adopted in October 2008 ( http://www.epa.gov/air/ lead/actions.html ). The primary (health-based) standard is a maximum or not-to-be-exceeded, rolling three-month average, measured as total suspended pa rticles (TSP). The secondary (welfare-based) standard is identical to the primary standard.



### 3.2 Risk Characterization

This section presents the resu lts of the preliminary risk as sessment for the wood furniture source category. The basic chronic inhalation ri sk estimates presented here are the maximum individual lifetime cancer risk, the maximum chr onic hazard index, and the cancer incidence. We also present results from our acute inhalati on impact assessments in the form of maximum hazard quotients, as well as the results of our screen for potential non-inhalation risks from PB HAP. Also presented are the HAP “dri vers,” which are the HAP that collectively contribute 90 percent of the maximum cancer ri sk or maximum hazard at the highest exposure location, as well as a summary of the results of our facility-wide assessments and our analysis

of risks associated with the maximum allowed emissions under the current MACT standards. A detailed summary of the facility-specific risk assessment results is av ailable in Appendix 6.

Tables 3.2-1 and 3.2-2 summarize the preliminary chronic and acute inhalation risk results for this source category. The results for the wood furniture manufacturing source category

indicate that maximum lifetime i ndividual cancer risks (the MIR) could be as high as 20 in a

million 19 . The major contributors to the risks ar e formaldehyde, bis(2-ethylhexyl)phthalate, and ethyl benzene. Approximately 20 people we re estimated to have cancer risks above 10 in

a million as a result of the emissions from 3 facilities, and approximately 20,000 people were

estimated to have cancer risks above 1 in a million as a result of the emissions from 64 facilities. The total cancer incidence associated with the source category was estimated to be

0.005 20

.

19 We note that this MIR value would be reduced by 50 percent if the CIIT URE for formaldehyde were used

instead of the IRIS URE.

20 We note that the cancer incidence would be reduced by 30 percent if the CIIT URE for formaldehyde were

used instead of the IRIS URE.29

Table 3.2-1 Summary of Source Category Le vel Inhalation Risks for Wood Furniture Manufacturing Facilities

Result HAP “Drivers” Facilities in Source Category

Number of Facilities Estimated to be in Source Category 406 n/a

Number of Facilities Identified in NEI and Modeled in Preliminary Risk Assessment 384 n/a

Cancer Risks Maximum Individual Lifetime Cancer Risk (in 1 million) 10 21 ethyl benzene, formaldehyde

Number of Facilities with Maximum Individual Lifetime Cancer Risk: Greater than or equal to 100 in 1 million 0 n/a Greater than or equal to 10 in 1 million 2 ethyl benzene, formaldehyde

Greater than or equal to 1 in 1 million 63 ethyl benzene, formaldehyde, methylene chloride, acrylonitrile, bis(2- ethylhexyl)phthalate, 1,4-dioxane, naphthalene

Chronic Noncancer Risks Maximum Hazard Index (Neurological) 0.4 n-hexane Number of Facilities with Hazard Index:

Greater than 1 0 n/a Acute Noncancer Screening Results

Maximum Acute Hazard Quotient 20 formaldehyde (REL)

Number of Facilities With Potential for Acute

Effects 25 formaldehyde, propyl cellosolve, toluene

Acute Noncancer Refined Results

Maximum Acute Hazard Quotient 10

7 / 0.35

2 / 0.35 propyl cellosolve (REL) formaldehyde (REL / AEGL1) toluene (REL / AEGL1)

Number of Facilities with Potential for Acute

Effects 19 propyl cellosolve, formaldehyde, toluene

Population Exposure Number of People Living Within 50 Kilometers

of Facilities Modeled 103,000,000 n/a

21 We note that the MIR for this source category would not change if the CIIT URE for formaldehyde had been used in the assessment, although the total cancer incide nce would decrease by 30%. There is an ongoing IRIS reassessment for formaldehyde, and future RTR risk asse ssments will use the cancer potency for formaldehyde that results from that reassessment. As a result, the current results may not match those of future assessments.30

Table 3.2-1 Summary of Source Category Le vel Inhalation Risks for Wood Furniture Manufacturing Facilities

Result HAP “Drivers”
Number of People Exposed to Cancer Risk: Greater than or equal to 100 in 1 million 0 n/a Greater than or equal to 10 in 1 million 7 n/a Greater than or equal to 1 in 1 million 20,000 n/a Number of People Exposed to Noncancer Hazard Index:

Greater than 1 0 n/a

Estimated Cancer Incidence (excess cancer cases per year) 0.005 19 n/a

Contribution of HAP to Cancer Incidence ethyl benzene 28% n/a bis(2-ethylhexyl)phthalate 28% n/a formaldehyde 28% n/a methylene chloride 6% n/a naphthalene 5% n/a 1,4-dioxane 3% n/a acrylonitrile 3% n/a

Table 3.2-2: Summary of NPRM Acut e Refined Results for Wood Furniture Manufacturing Facilities

Screening Results MAXIMUM ACUTE HAZARD QUOTIENTS ACUTE DOSE-RESPONSE VALUES

HAP Max. 1-hr.

Air Conc.

(mg/m 3 ) Based on

REL Based on

AEGL-1/

ERPG-1 Based on

AEGL-2/

ERPG-2 REL (mg/m 3 ) AEGL-1 (1-hr) (mg/m 3 ) ERPG-1 (mg/m 3 ) AEGL-2 (1-hr) (mg/m 3 ) ERPG-2 (mg/m 3 )

Formaldehyde 0.39 7 0.35 0.02 0.055 1.1 1.1 17 17 Propyl

Cellosolve 0.93 10 n/a n/a 0.093

Toluene 67 2 0.09/0.35 0.01/0.06 37 750 190 4500 1130

Notes on Refined Process: 1) The screening was performed for all emitted HAP with availabl e acute dose-response values. Only those pollutants whose screening HQs were greater than 1 for at least one acute threshold value are shown in the table. 2) HAP with available acute dose-response valu es which are not in the table do not carry any potential for posing acute health ris ks, based on an analysis of curren tly available emissions data. Notes on Acute Dose-Response Values: REL – California EPA reference exposure level for no adverse effects. Most, but not all, RELs are for 1-hour exposures. AEGL – Acute exposure guideline levels represent emer gency exposure (1-hour) limits for the general public. AEGL-1 is the exposure level above which it is predicted that the general population, including susceptible individuals, could experience effects that are notable discomfort, but whic h are transient and reversible upon cessation of exposure. AEGL-2 is the exposure level above which it is predicted that the general population, including susceptible individuals, could experience irreversible or other seriou s, long-lasting adverse health effects of an impaired ability to escape. EPRG – Emergency Removal Program gui delines represent emergency exposure (1-hour) limits for the general public.

ERPG-1 is the maximum level below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing other than mild, tran sient adverse health effects.31

ERPG-2 is the maximum exposure below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing or developing irreversible or other serious health effects or symptoms which could impair an individual’s ability to take protective action.

The maximum chronic non-cancer TOSHI values for the source category could be as high as 0.4, with emissions of n-hexane dominating thos e impacts, indicating no significant potential for chronic noncancer impacts. The estimati on of chronic non-cancer TOSHI values was performed consistent with the approach taken in previous risk and tec hnology review for other source categories, i.e., we used our existi ng hierarchy of refere nce values (EPA 1999 – Residual Risk Report to Congress) , which favors the use of an IR IS value when available, and favors using values which have been developed and peer-reviewed using processes similar to the IRIS process under the sponsorship of a state or federal government agency, the documentation of which can be easily accessed by the public (such as those from ATSDR or the California EPA) when IRIS values are not available. The use of a surrogate reference value for chemicals in a chemical group (e.g., glyc ol ethers) is part of this approach when specific chemicals in the group do not have ava ilable reference values, and/or emissions are reported generically for the chemical group and not specific chemicals. In this case, the IRIS RfC for ethylene glycol m onomethyl ether (EGME) is the lowe st (i.e., most health protective) of the available reference values for glycol et hers from our hierarchy of reference values. Using the surrogate approach described a bove, the maximum chroni c non-cancer Target Organ-Specific Hazard Index (HI) for the source category could be as high as 0.4 (based on actual emissions) and 0.8 (based on allowable emissions), with emissions of n-hexane dominating.

In reviewing data sources for this residual risk assessment, we identified a Provisional Peer Reviewed Toxicity Value (PPRTV) for assess ing chronic noncancer health risks from inhalation of diethylene glyc ol monobutyl ether (DGBE), which is emitted by some facilities in this source category. PPRTVs are referen ce values, developed by EPA for use specifically in EPA’s Superfund Program when an acceptable reference value, such as those found in EPA’s IRIS database, is not otherwise available.

The DGBE PPRTV was prepared for EPA’s Supe rfund Program in 2009. Inhalation toxicity information for DGBE is essentially limited to the results of a single 5-week study in rats (Gushow et al., 1984), which resulted in slight v acuolization of the liver cells consistent with fatty change. An uncertainty factor of 3000 was applied in deriving the PPRTV, and confidence in the provisional RfC (p-RfC) value is low.

PPRTVs differ from IRIS values in that PPRTVs do not receive the multiprogram review provided for IRIS values. As stated in the DGBE PPRTV document, this is because “...IRIS values are generally intended to be used in all U.S. EPA programs, while PPRTVs are developed specifically for the Superfund Program.”

The EPA’s Superfund Program uses PPRTVs in c onjunction with assessments to support sitespecific clean-up decisions. PPRTVs are applie d to high quality exposure data developed for each Superfund site using measurements of th e specific chemical for which the PPRTV was developed. Each final cleanup decision, as memori alized in a Record of Decision, is subject to public notice and comment, and it is at this stag e of the process that a public review of how32

a PPRTV was used in that site-specific contex t may occur, which may include consideration of comments on the development of the PPRTV itself (i.e., the PPRTV development document is not explicitly the subject of a se parate public review or comment period). The current process for development of the refere nce values used to support today’s proposed decisions includes a public comment period prior to a final external peer review of the assessment. This more rigorous review process prior to the release of the values enables

immediate use of the derived values acro ss multiple EPA Program Offices, including providing support for national re gulatory decisions (e.g., RTR).

Contrasting the site-specific Superfund app lication of PPRTVs and related Records of Decision, the Wood Furniture RTR proposal is of national scope and will not be subject to ongoing review related to each application to a facility. Based on the foregoing discussion,

EPA has determined that reliance on the DGBE PP RTV value in this RTR rule is beyond the specific purpose for which it was developed, and would exacerbate the cumulative uncertainty in the risk assessment stemming from limitations in the underlying exposure and toxicity data. Accordingly, EPA has not used the DGBE PPRTV value in the baseline Wood Furniture risk assessment supporting this proposed action, noting that a suitable alternative value (in this case, it is the RfC for EGME from IRIS) is availa ble to represent the toxi city of glycol ethers without hierarchically based noncancer reference values in the assessment.

The acute screening risk assessment for this source category identified 25 facilities with a potential HQ greater than 1, with a maximu m HQ up to 20. We performed refined acute assessments for these facilities with improved mapping and emission source modeling, resulting in a maximum estimated off-site HQ of 10, with 20 facilities having an HQ estimated to be greater than 1. The primar y acute risk drivers were glycol ethers, formaldehyde, and toluene, and all the HQ es timates greater than 1 were based on REL values. Table 3.2-2 shows that the maximum acute HQ values for formaldehyde and toluene based on ERPG-1 values were 0.35 and 0.35, re spectively, and that the maximum acute HQ

values based on AEGL-1 values were 0.35 and 0.09, respectively. There are no other appropriate acute values for glyc ol ethers on which to base a comparison or interpretation of potential risk. Refer to Appe ndix 7 for detailed results of the refined acute assessments.

Based on the REL value for formaldehyde, an o ff-site acute HQ value from a wood furniture manufacturing facility could be up to 7. We also estimate that emissions of formaldehyde from 11 facilities (or about 3 percent of the to tal facilities) may result in maximum acute HQ REL values greater than 1. The REL is consid ered protective; exposures above the REL do not necessarily indicate that adverse effects w ill occur. The AEGL-1 and ERPG-1 values are those that when exceeded can produce mild a nd progressively increasing but transient and nondisabling sensory irritation or certain asymptomatic, nonsensory effects. . The maximum acute HQ for formaldehyde based on an AEGL-1 or ERPG-1 value is 0.35, indicating that these effects (which are generally eye and nose ir ritation) might not be ex pected to occur.

To better characterize the potential health ri sks associated with estimated worst-case acute exposures to HAP, and in response to a key recommendation from the Science Advisory33

Board’s peer review of EPA’s RTR risk assessment methodologies 22 , we examine a wider range of available acute health metrics than we do for our chronic risk assessments. This is in response to the acknowledgement that there are ge nerally more data gaps and inconsistencies

in acute reference values than there are in ch ronic reference values. By definition, the acute CA-REL represents a health-prot ective level of exposure, with no risk anticipated below those levels, even for repeated exposures; however, the health risk from higher-level exposures is unknown. Therefore, when a CA-REL is exceeded and an AEGL-1 or ERPG-1 level is available (i.e., levels at which mild effects ar e anticipated in the general public for a single exposure), we have used them as a second comp arative measure. Historically, comparisons of the estimated maximum off-site one-hour exposur e levels have not been typically made to occupational levels for the purpose of characte rizing public health risks in RTR assessments. This is because occupational ce iling values are not generally considered protective for the general public since they are designed to protect the worker population (presumed healthy adults) for short duration (<15 minute) increases in exposure 21 . As a result, for most chemicals, the 15-minute occupational ceiling valu es are set at levels higher than a one-hour AEGL-1, making comparisons to them irrelevant unless the AEGL-1 or ERPG-1 levels are

exceeded 21 . Such is not the case when comparing the available acute inhalation health effect reference values for formaldehyde 23

.

The worst-case maximum estimated 1-hour exposure to formaldehyde outside the facility fence line for this source category is 0.47 mg/m 3 . This estimated worst-case exposure exceeds the 1-hour REL by a factor of 8 (HQ REL = 8) and is below the 1-hour AEGL-1 (HQ AEGL-1 = 0.4). Although this exposure estimate does not exceed the AEGL-1, it exceeds the workplace ceiling leve l guideline for the value developed by the NIOSH 24 “for any 15 minute period in a work day” (N IOSH REL-ceiling value of 0.12 mg/m 3 ; HQ NIOSH = 4). Additionally, the estimated maximum acute exposure exceeds the Air Quality Guideline value that was developed by the World Health Organization 25 for 30-minute exposures (0.1 mg/m 3 ; HQ WHO = 5). The estimated HQ equals 1 when the ACGIH TLV-Ceiling value (0.37 mg/m 3 ),

22 The SAB peer review of RTR Risk Assessment Methodologies is available at:

http://yosemite.epa.gov/sab/sabproduct.nsf/4AB39 66E263D943A8525771F00668381/$File/EPA-SAB-10- 007-unsigned.pdf

23 U.S. EPA. (2009) Chapter 2.9 Chemical Specific Refere nce Values for Formaldehyde in Graphical Arrays of

Chemical-Specific Health Effect Reference Values for Inhalation Exposures (Final Report). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-09/061, and available on-line at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003

24 National Institutes for Occupational Safety and Health (NIOSH). Occupational Safe ty and Health Guideline

for Formaldehyde; http://www.cdc.gov/niosh/docs/81-123/pdfs/0293.pdf

25 WHO (2000). Chapter 5.8 Formaldehyde, in Air Quality Guidelines for Europe, second edition. World Health Organization Regional Publications, European Series, No. 91. Copenhagen, Denmark. Available on-line at http://www.euro.who.int/__data/assets/pdf_file/0005/74732/E71922.pdf

.34

a value defined as “not to be exceeded at any time,” 26 is compared to the worst-case acute exposure screening level 27

.

As noted in Table 3.2-2, using the REL to assess concentrations from one of the glycol ethers, propyl cellosolve, an off-site acute non-cancer HQ value up to 10 could occur based upon the use of the acute dose-response value of EGME. Exposures above the REL do not necessarily

indicate that adverse effects will occur, only that they cannot be ruled out. We estimate that emissions of propyl cellosolve from 8 facilities (or about 2 percen t of the total facilities) may result in maximum acute HQ values greater than 1.

In characterizing the potential acute noncancer ri sks from propyl celloso lve, a glycol ether HAP, it is important to consider the uncertainties related to the emissi ons inventory. First, there is uncertainty in the speciation profile developed for glycol et hers for this source category based upon extrapolating speciated glycol ether emissions from 26 facilities to the remaining 66 facilities that report ed only total glycol ether emissi ons rather than emissions of individual glycol ether species. This profile may not be repres entative of the source category

since it is based on data from a limited subset of facilities. Second, these uncertainties are compounded in our attempt to assess risks for those glycol ether compounds for which we do not have dose-response values.

When interpreting the chronic cancer risk resu lts and potential acute risks from formaldehyde emissions from this source category, it is impor tant to note that th e reported levels of formaldehyde in the NEI are likely derived from coatings content and therefore may not fully account for gluing operations or curing proce sses that may also create formaldehyde. Recognizing that there is no approved method for estimating formaldehyde emissions from curing, this is an uncertainty that is likely to bi as our risk assessment low. The extent of such underestimation, if any, however, is unknown.

In performing the screening for potential multi-pa thway exposures and risks of concern, we

determined that emissions of cadmium and POM were not significant enough to pose multipathway impacts of concern for human health or the environment. We also determined that emissions of lead were not significant enough to cause an exceedance of the lead NAAQS, indicating that potential multipathway impacts fo r lead from this source category are not significant.

The results of the facility-wide assessments are summarized in Table 3.2-3. The results indicate that one facility ha s the highest cancer MIR of 100 in a million, 2 facilities have a

cancer MIR between 10 and 100 in a million. All ot her facility MIR values were equal to or less than 10 in a million. The source category MIR at the one facility w ith the highest MIR is 1 in a million, contributing only 1% to the facility -wide cancer risk. Add itionally, this facility with the highest MIR also has the highest f acility-wide HI of 3 with a source category HI

26 ACGIH (2001) Formaldehyde. In Documentation of the TLVs and BEIs with other Worldwide Occupational Exposure Values. ACGIH, 1300 Kemper Meadow Drive, Cincinnati, OH 45240(ISBN: 978-1-882417-74-2) and available on-line at http://www.acgih.org.

27 EPA considers this HQ of 1, not to represent an exceedance for purposes of the RTR program.35

contribution of 13%. In general, facility-wide ca ncer risks are low (MIR less than or equal to 1) for the source category, and the contribution of the source cate gory to overall facility risks

is close to 100% for most of the facilities with facility-wide MIR values greater than 1 in this
source category.

Table 3.2-3 Wood Furniture Source Category Contribution to Facility-Wide Cancer

Risks

Number of Facilities Binned by Facility-Wide MIR (in 1 million) Source Category MIR Contribution to Facility-Wide MIR < 1 ≥ 1 <10 ≥ 10 < 100 > 100 Total

100% 95 48 2 0 145

90-99% 45 7 0 0 52

50-89% 21 6 0 0 27

10-49% 9 6 0 0 15

1-9% 0 1 0 1 2

0% 141 2 0 0 143

Total 311 70 2 1 384

Analysis of potential differences between act ual emission levels and the maximum emissions

allowable under the MACT standards for wood furniture manufacturers indicated that MACT allowable emission levels may be up to 2 tim es greater than actual emission levels. Considering this difference, the risk results fr om the inhalation risk assessment indicate that

the maximum lifetime individual cancer risk could be up to 40 in a million and that the maximum chronic noncancer TOSHI could be up to 0.8 for this source category with all facilities operating at MACT-allowable emission levels. Fo r more detail about this estimate

of the ratio of actual to MA CT-allowable emissions, for th e wood furniture manufacturing source category, see the memo to the rulemaking docket [ 31 ].



### 4 General Discussion of Uncertaint ies and How They Have Been

Addressed

4.1 Exposure Modeling Uncertainties

Although every effort has been made to identify a ll the relevant faciliti es and emission points, as well as to develop accurate estimates of the annual emission rates for all relevant HAP, the uncertainties in our emission inventory likely dominate the uncertainties in our exposure modeling estimates. The chronic exposure modeli ng uncertainties are considered relatively small in comparison, since we are using EP A’s refined local dispersion model with reasonably representative meteorology. It is po ssible that population exposure estimates are also biased slightly higher by not accounting fo r short- or long-term population mobility, and by neglecting processes like deposition, plume depletion, and atmospheric degradation.36

Additionally, estimates of the maximum individu al risk (MIR) contain uncertainty, because they are derived at census bloc k centroid locations rather th an actual residences. This uncertainty is known to create potential underes timates and overestimates of the actual MIR values for individual facilities, but, overall, it is not thought to have a significant impact on the estimated MIR for a source category. Finally, we did not factor in the possibility of a source closure occurring during the 70-year chronic exposure period, leading to a potential upward bias in both the MIR and population risk estimates; nor did we factor in the possibility of population growth during th e 70-year chronic exposure period, leading to a potential

downward bias in both the MIR and population risk estimates.

A sensitivity analysis performed for the 1999 NATA found that th e selection of the meteorology dataset location could result in a ra nge of chronic ambient concentrations which varied from as much as 17% below the predic ted value to as much as 84% higher than the predicted value. This va riability translates dir ectly to the predicted exposures and risks in our assessment, indicating that the actual risks could vary from 17 % lower to 84% higher than the predicted values.

We have purposely biased the acute screening results high, consideri ng that they depend upon the joint occurrence of independent factors, su ch as hourly emissions rates, meteorology and human activity patterns. Furthermore, in cas es where multiple acute threshold values are considered scientifically accep table we have chosen the most conservative of these assessments, erring on the side of overestimating potential health risks from acute exposures. In the cases where these results indicated the potential for exceeding short-term health thresholds, we have refined our assessmen t by developing a better understanding of the geography of the facility relative to potential exposure locations and the true variability of

short-term emission rates. For this source cate gory, refining the assessmen ts for the facilities

that did not screen out resulted in estimates of acute risks which were the same or lower than those from the screening assessment.



### 4.2 Uncertainties in the Dose -Response Re lationships

In the sections that follow, se parate discussions are provided on uncertainty associated with cancer potency factors and for non-cancer refe rence values. Cancer potency values are derived for chronic (lifetime) exposures. Non- cancer reference values are generally derived for chronic exposures (up to a lifetime), but may also be derived for acute (

 <24 hours), shortterm (>24 hours up to 30 days), and subchronic (>30 days up to 10% of lifetime) exposure durations, all of which are derived based on an assumption of con tinuous exposure throughout the duration specified. For the purposes of assess ing all potential health risks associated with the emissions included in an assessment, we rely on both chronic (cancer and non-cancer) and acute (non-cancer) benchmarks, which are described in more detail below.

Although every effort is made to identify p eer-reviewed dose-respons e values for all HAPs emitted by the sources included in an assessment, some HAP have no peer-reviewed cancer potency values or reference values for chroni c non-cancer or acute e ffects (inhalation or ingestion). Since exposures to these pollutants cannot be incl uded in a quantitative risk37

estimate, an understatement of risk for these po llutants at environmental exposure levels is possible.

Additionally, chronic dose -response values for certain compo unds included in the assessment may be under EPA IRIS review and revised asse ssments may determine that these pollutants are more or less potent than currently thought. We will re-evaluate risks if, as a result of these reviews, a dose-response metric changes enough to indicate that the risk assessment may significantly mischaracter ize human health risk.

Cancer assessment

The discussion of dose-response uncertainties in the estimation of cancer risk below focuses on the uncertainties associated with the specifi c approach currently used by the EPA to develop cancer potency factors. In general, these same uncertainties attend the development of cancer potency factors by CalEPA, the sour ce of peer-reviewed cancer potency factors used where EPA-developed values are not yet ava ilable. To place this discussion in context, we provide a quote from the EPA’s Guidelines for Carcinogen Risk Assessment [ 32 ] (herein

referred to as Cancer Guidelines ). “The primary goal of EPA actions is prot ection of human health; accordingly, as an Agency policy, ri sk assessment procedures, including default options that are used in the absence of scien tific data to the cont rary, should be health protective.” The approach adopt ed in this document is consis tent with this approach as

described in the Cancer Guidelines

.

For cancer endpoints EPA usually derives an oral slope factor for inge stion and a unit risk value for inhalation exposures. These values allow estimation of a lifetime probability of developing cancer given long-term exposures to the pollutant. Depending on the pollutant being evaluated, EPA relies on both animal bioassay and epidemiological studies to

characterize cancer risk. As a science policy approach, consistent with the Cancer

Guidelines , EPA uses animal cancer bioassays as i ndicators of potential human health risk

when other human cancer risk data are unavailable.

Extrapolation of study data to estimate poten tial risks to human populations is based upon

EPA’s assessment of the scientific database for a pollutant using EPA’s guidance documents and other peer-reviewed methodologies. The EPA Cancer Guidelines describes the Agency’s recommendations for methodologies for cancer ri sk assessment. EPA believes that cancer risk estimates developed following the procedures described in the Cancer Guidelines and outlined below generally provide an upper bound estimate of risk. That is, EPA’s upper bound estimates represent a “plausible upper limit to the true value of a quantity” (although this is usually not a true statistical confidence limit). 28 In some circumstances, the true risk could be as low as zero; howev er, in other circumstances the risk could also be greater. 29
When developing an upper bound estimate of risk and to provide risk values that do not

28 IRIS glossary (www.epa.gov/NCEA/iris/help_gloss.htm).

29 The exception to this is the URE for benzene, which is considered to cover a range of values, each end of which is considered to be equally plausible, an d which is based on maximum likelihood estimates.38

underestimate risk, EPA generally relies on conservative default approaches. 30 EPA also uses the upper bound (rather than lower bound or central) estimates in its assessments, although it is noted that this approach can have limitations for some uses (e.g. priority setting, expected benefits analysis).

Such health risk assessments have associated uncertainties, some wh ich may be considered quantitatively, and others which generally are ex pressed qualitatively. Un certainties may vary substantially among cancer risk a ssessments associated with expos ures to different pollutants, since the assessments employ different database s with different strengt hs and limitations and the procedures employed may differ in how well they represent actual biological processes for

the assessed substance. EPA’s Risk Characterization Handbook also recommends that risk characterizations present estimat es demonstrating the impact on the assessment of alternative choices, data, models and assumptions [ 33 ]. Some of the major sources of uncertainty and variability in deriving cancer risk valu es are described more fully below.

(1) The qualitative similarities or differen ces between tumor responses observed in experimental animal bioassays and those wh ich would occur in humans is a source of uncertainty in cancer risk assessment. In ge neral, EPA does not assume that tumor sites observed in an experimental an imal bioassay are necessarily predictive of the sites at which

tumors would occur in humans. 31 However, unless scientific support is ava ilable to show otherwise, EPA assumes that tumors in animals are relevant in humans, regardless of target organ concordance. For a specific pollutant, qua litative differences in species responses can

lead to either under-estimation or over-e stimation of human cancer risks.

(2) Uncertainties regarding the most appropriate dose metric for an assessment can also lead to differences in risk predictions. For exam ple, the measure of dose is commonly expressed in units of mg/kg/d ingested or the inhaled con centration of the pollutant. However, data may support development of a pharmacokinetic mode l for the absorption, distribution, metabolism and excretion of an agent, which may resu lt in improved dose metrics (e.g., average blood concentration of the pollutant or the quantity of agent metabolized in the body). Quantitative uncertainties result when the appropriate choi ce of a dose metric is uncertain or when dose

metric estimates are themselves uncerta in (e.g., as can occur when alternative pharmacokinetic models are available for a co mpound). Uncertainty in dose estimates may

lead to either over or underestimation of risk.

30 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are generic approaches, based on general scientific knowle dge and policy judgment, that are applied to various elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions

are used to ensure that risk to chemicals is not under estimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An Examination of EPA Risk Asse ssment Principles and Practices, EPA/100/B-04/001 available at: http://w ww.epa.gov/osa/pdfs/ratf-final.pdf.

31 Per the EPA Cancer Guidelines: “The default option is that positive effects in animal cancer studies indicate that the agent under study can have carcinogenic potential in humans.” and “Target organ concordance is not a prerequisite for evaluating the implications of animal study results for humans.”39

(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to humans, EPA uses scaling met hodologies (relating expected response to differences in physical size of the species), which introdu ce another source of uncertainty. These methodologies are based on both biol ogical data on differences in rates of process according to species size and empirical comparisons of toxicity between expe rimental animals and humans. For a particular pollutant, the quant itative difference in cancer potency between experimental animals and humans may be either greater than or less than that estimated by baseline scientific scaling predictions due to uncertainties associated with limitations in the

test data and the correctness of scaled estimates.

(4) EPA cancer risk estimates, whether based on epidemiological or experimental animal data, are generally developed using a benchmark dose (BMD) analysis to estimate a dose at which there is a specified excess risk of cancer, which is used as the point of departure (or POD) for

the remainder of the calculat ion. Statistical uncertainty in developing a POD using a benchmark dose (BMD) approach is generally addressed though use of the 95% lower confidence limit on the dose at which the sp ecified excess risk occurs (the BMDL), decreasing the likelihood of understating risk. EPA has generally utilized the multistage model for estimation of the BMDL using cancer bioassay data (see furt her discussion below).

(5) Extrapolation from high to low doses is an important, and potential ly large, source of uncertainty in cancer risk assessment. EPA uses different approaches to low dose risk assessment (i.e., developing estimates of risk for exposures to environmental doses of an agent from observations in experimental or ep idemiological studies at higher dose) depending

on the available data and understa nding of a pollutant’s mode of action (i.e., the manner in which a pollutant causes cancer). EPA’s Cancer Guidelines express a preference for the use of reliable, compound-specific, bi ologically-based risk models when feasible; however, such models are rarely available. The mode of action for a pollutant (i.e., the manner in which a pollutant causes cancer) is a key considerati on in determining how ri sks should be estimated for low-dose exposure. A reference value is calculated when the available mode of action data show the response to be nonlinear (e.g., as in a threshold response). A linear low-dose (straight line from POD) approach is used when available mode of action data support a linear (e.g., nonthreshold response) or as the most common default approach when a compound’s

mode of action is unknown. Lin ear extrapolation can be suppor ted by both pollutant-specific

data and broader scientific cons iderations. For example, EPA’s Cancer Guidelines generally consider a linear dose-response to be appropria te for pollutants that interact with DNA and

induce mutations. Pollutants whose effects ar e additive to background biological processes in cancer development can also be predicted to have low-dose linear responses, although the slope of this relationship may not be the same as the slope estimate d by the straight line approach.

EPA most frequently utilizes a linear low-dose extrapolation appr oach as a baseline sciencepolicy choice (a “default”) when available data do not allow a compound-specific determination. This approach is designed to not underestimate risk in the face of uncertainty and variability. EPA believes that linear dose-response models , when appropriately applied as part of EPA’s cancer risk assessment proce ss, provide an upper bound estimate of risk and40

generally provide a health protec tive approach. Note that anothe r source of uncertainty is the

characterization of low-dose non linear, non-threshold relationships. The National Academy of Sciences has encouraged the exploration of sigmoidal type functions (e.g., log-probit models) in representing dose res ponse relationships due to the variability in response within human populations. Another National Research Council (NRC) report [ 34 ] suggests that

models based on distributions of individual thresholds are likely to lead to sigmoidal-shaped dose-response functions for a population. This report notes sources of variability in the human population: “One might expect these in dividual tolerances to vary extensively in humans depending on genetics, co incident exposures, nutritional status, and various other susceptibility factors...” Thus , if a distribution of thresholds approach is considered for a carcinogen risk assessment, application would depend on ability of modeling to reflect the degree of variability in respons e in human populations (as opposed to responses in bioassays with genetically more uniform rodents). Note al so that low dose linearity in risk can arise for reasons separate from population variability: du e to the nature of a mode of action and additivity of a chemical’s effect on top of background chemical exposures and biological

processes.

As noted above, EPA’s current approach to cancer risk assessment typically utilizes a straight line approach from the BMDL. This is equiva lent to using an upper confidence limit on the slope of the straight line extr apolation. The impact of the choice of the BMDL on bottom line risk estimates can be quantified by comparing ri sk estimates using the BMDL value to central estimate BMD values, although these differences are generally not a large contributor to uncertainty in risk assessment (Subramaniam et. al., 2006) [ 35 ]. It is important to note that earlier EPA assessments, including the majority of those for which risk values exist today, were generally developed using the multistage model to extrapolate down to environmental

dose levels and did not involve the use of a POD. Subramaniam et. al. (2006) also provide comparisons indicating that slopes based on st raight line extrapolation from a POD do not show large differences from those based on the upper confidence limit of the multistage

model.

(6) Cancer risk estimates do not generally make specific adjustments to reflect the variability in response within the human population — resu lting in another source of uncertainty in assessments. In the diverse human population, some individuals are likely to be more sensitive to the action of a carcinogen than the typical individual , although compound-specific data to evaluate this variability are generally not available. There may also be important life stage differences in the quantitative potency of carcinogens and, with the exception of the recommendations in EPA’s Supplemental Cancer Guidance for carcinogens with a mutagenic mode of action, risk assessments do not generally quantitatively address life stage differences. However, one approach used commonly in EPA assessments that may help address variability in response is to extrapolate human response fr om results observed in the most sensitive species and sex tested, resulting typically in the highest URE which can be supported by reliable data, thus supporting estimates that ar e designed not to underestimate risk in the face of uncertainty and variability.

Chronic non-cancer assessment41

Chronic non-cancer reference values represent chronic exposure leve ls that are intended to be health-protective. That is, EPA and other orga nizations which develop non-cancer reference values (e.g., the Agency for Toxic Substances and Disease Registry – ATSDR) utilize an approach that is intended not to underestimate risk in the face of uncertainty and variability. When there are gaps in the available informa tion, uncertainty factors (UFs) are applied to

derive reference values that are intended to be protective against appreciable risk of deleterious effects. Uncertainty factors are commonly default values 32 e.g., factors of 10 or 3, used in the absence of compound-specific data; wh ere data are available, uncertainty factors may also be developed using compound-specific information. When data are limited, more assumptions are needed and more default fact ors are used. Thus there may be a greater tendency to overestimate risk—in the sense th at further study might support development of reference values that are higher (i.e., less pot ent) because fewer default assumptions are needed. However, for some pollutants it is possible that risks ma y be underestimated.

For non-cancer endpoints related to chronic exposures, EPA derives a Reference Dose (RfD) for exposures via ingestion, and a Reference C oncentration (RfC) for inhalation exposures. These values provide an estimate (with uncerta inty spanning perhaps an order of magnitude) of daily oral exposure (RfD) or of a conti nuous inhalation exposure (RfC) to the human population (including sensitive subgroups) that is lik ely to be without an appreciable risk of deleterious effects during a lifetime. 33 To derive values that are intended to be “without appreciable risk,” EPA’s met hodology relies upon an uncertain ty factor (UF) approach [ 36 ],[ 37 ]which includes consideration of both uncertainty and variability.

EPA begins by evaluating all of the availabl e peer-reviewed literature to determine noncancer endpoints of concern, eval uating the quality, strengths and limitations of the available studies. EPA typically chooses th e relevant endpoint that occu rs at the lowest dose, often using statistical modeling of the available data, and then determines th e appropriate point of departure (POD) for derivation of the reference value. A POD is determined by (in order of preference): (1) a statistical estimation using the benchmark dos e (BMD) approach; (2) use of

the dose or concentration at which the toxic response was not signi ficantly elevated (no observed adverse effect level— NOAEL); or (3) use of the lowest observed adverse effect level (LOAEL).

32 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are generic approaches, based on general scientific knowle dge and policy judgment, that are applied to various elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions

are used to ensure that risk to chemicals is not under estimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An examination of EPA Risk Assess ment Principles and Practices, EPA/100/B-04/001 available at: http://w ww.epa.gov/osa/pdfs/ratf-final.pdf.

33 See IRIS glossary42

A series of downward adjustments using defau lt UFs is then applied to the POD to estimate the reference value,[ 38 ]. While collectively termed “U Fs”, these factors account for a number of different quantitativ e considerations when utiliz ing observed animal (usually rodent) or human toxicity data in a risk assessment. The UFs are intended to account for: (1) variation in susceptibility among the members of the human population (i.e., inter-in variability); (2) uncertainty in extrapolating fr om experimental animal data to humans (i.e., interspecies differences); (3) uncertainty in ex trapolating from data obtained in a study with less-than-lifetime exposure (i.e., extrapolati ng from subchronic to chronic exposure); (4) uncertainty in extrapolating from a LOAEL in the absence of a NOAEL; and (5) uncertainty when the database is incomplete or there are pr oblems with applicability of available studies. When scientifically sound, peer-reviewed assess ment-specific data are not available, default adjustment values are selected for the indivi dual UFs. For each type of uncertainty (when relevant to the assessment), EPA typically applie s an UF value of 10 or 3 with the cumulative UF value leading to a downward adjustment of 10-3000 fold from the selected POD. An UF of 3 is used when the data do not support the use of a 10-fold factor. If an extrapolation step or adjustment is not relevant to an assessment (e.g., if applying human toxicity data and an interspecies extrapolation is not required) the associated UF is not used. The major adjustment steps are described more fully below. dividual

1) Heterogeneity among humans is a key sour ce of variability as well as uncertainty. Uncertainty related to human variation is consid ered in extrapolating doses from a subset or smaller-sized population, often of one sex or of a narrow range of life stages (typical of occupational epidemiologic studies), to a larger , more diverse population. In the absence of pollutant-specific data on human variation, a 10 -fold UF is used to account for uncertainty

associated with human variation. Human variatio n may be larger or smaller; however, data to examine the potential magnitude of human va riability are often una vailable. In some situations, a smaller UF of 3 ma y be applied to reflect a known l ack of significant variability among humans.

2) Extrapolation from results of studie s in experimental animals to humans is a necessary step for the majority of chemical ri sk assessments. When interpreting animal data, the concentration at the POD (e.g. NOAEL, BM DL) in an animal model (e.g. rodents) is extrapolated to estimate the human response. While there is long-sta nding scientific support

for the use of animal studies as indicators of potential toxicity to humans, there are uncertainties in such extrapolat ions. In the absence of data to the contrary, the typical approach is to use the most relevant endpoint from the most sensitive species and the most sensitive sex in assessing risks to the aver age human. Typically, compound specific data to evaluate relative sensitivity in humans versus rodents are lacki ng, thus leading to uncertainty in this extrapolation. Size-related differences (allometric relationships) indicate that typically

humans are more sensitive than rodents when compared on a mg/kg/day basis. The default choice of 10 for the interspecies UF is consis tent with these differences. For a specific chemical, differences in species responses may be greater or less than this value.

Pharmacokinetic models are useful to examine species differences in pharmacokinetic processing and associated uncertainties; however , such dosimetric adjustments are not always possible. Information may not be available to quantitatively assess toxicokinetic or43

toxicodynamic differences between animals a nd humans, and in many cases a 10-fold UF (with separate factors of 3 for toxicokinetic and toxicodynamic components) is used to account for expected species differences and as sociated uncertainty in extrapolating from laboratory animals to humans in the derivation of a reference value. If information on one or the other of these components is availabl e and accounted for in the cross-species extrapolation, a UF of 3 may be used for the remaining component.

3) In the case of reference values for ch ronic exposures where only data from shorter durations are available (e.g., 90-da y subchronic studies in rodent s) or when such data are judged more appropriate for development of an RfC, an additional UF of 3 or 10-fold is typically applied unless th e available scientific information supports use of a different value.

4) Toxicity data are typically limited as to the dose or exposure levels that have been tested in individual studies; in an animal study, for example, treatment groups may differ in exposure by up to an order of magnitude. The pref erred approach to arrive at a POD is to use BMD analysis; however, this approach requires ad equate quantitative results for a meaningful analysis, which is not always possible. Use of a NOAEL is the next preferred approach after BMD analysis in determining a POD for deriving a health effect reference value. However, many studies lack a dose or exposure level at wh ich an adverse effect is not observed (i.e., a NOAEL is not identified). When using data limited to a LOAEL, a UF of 10 or 3-fold is often applied.

5) The database UF is intended to a ccount for the potential for deriving an underprotective RfD/RfC due to a data gap pr eventing complete char acterization of the chemical’s toxicity. In the absence of studies for a known or suspecte d endpoint of concern, a UF of 10 or 3-fold is typically applied.

Acute noncancer assessment

Many of the UFs used to account for variability and uncertainty in the development of acute reference values are quite similar to those de veloped for chronic durations, but more often using individual UF values that may be less than 10. UFs are applied based on chemicalspecific or health effect-specific informati on (e.g., simple irritation effects do not vary appreciably between human indivi duals, hence a value of 3 is t ypically used), or based on the purpose for the reference value (see the followi ng paragraph). The UFs applied in acute reference value derivation include: 1) he terogeneity among humans ; 2) uncertainty in extrapolating from animals to humans; 3) unc ertainty in LOAEL to NOAEL adjustments; and 4) uncertainty in accounting for an incomplete database on toxic effect s of potential concern. Additional adjustments are often applied to a ccount for uncertainty in extrapolation from observations at one exposure dura tion (e.g., 4 hours) to arrive at a POD for derivation of an

acute reference value at another exposure duration (e.g., 1 hour).

Not all acute reference values are developed for the same purpose and care must be taken when interpreting the results of an acute asse ssment of human health effects relative to the reference value or values being exceeded. Wh ere relevant to the estimated exposures, the44

lack of threshold values at di fferent levels of severity shoul d be factored into the risk characterization as poten tial uncertainties.



### 5 References

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.

2 . US EPA, 2009. Risk and Technology Review (RTR) Risk Assessmen t Methodologies: For Review by the EPA’s Science Advisory Boar d with Case Studies – MACT I Petroleum Refining Sources and Portland Cement Manufacturing. EP A-452/R-09-006. http://www.epa.gov/ttn/atw/rrisk/rtrpg.html.

3 . US EPA, 2010. SAB’s Res ponse to EPA’s RTR Risk As sessment Methodologies. http://yosemite.epa.gov/sab/sabproduc t.nsf/4AB3966E263D943 A8525771F00668381/$File/E PA-SAB-10-007-unsigned.pdf

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Rule. 40 CFR Part 51. http://www.epa.gov/EPA-AIR/2005 /November/Day-09/a21627.htm

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7

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for Wood Furniture Manuf acturing Operations”

8. Allen, D., C. Murphy, Y. Kimura, W. Vizuete, T. Edgar, H. Jeffries, B.-U. Kim, M. Webster, and M. Symons, 2004. Variab le industrial VOC emissions and their impact on ozone

formation in the Houston Galveston Area. Final Report: Texas Environmental Research Consortium Project H-13. http://files.harc.edu/Pr ojects/AirQua lity/Projects/H013.2003/H13FinalReport.pdf

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20 . CA Office of Environmental He alth Hazard assessme nt, 2000. All Acute Reference Exposure Levels developed by OEHHA as of May 2000. http://www.oehha.ca.gov/air/acute_rels/allAcRELs.html

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EPA Office of Environmental In formation. EPA/260R-02-008. http://www.epa.gov/quality/inf ormationguidelines/documents/EP A_InfoQualityGuidelines.pdf

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14 June 2006. http://www.epa.gov/osa/spc/pdf s/CGIWGCommunication_II.pdf46

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EPA/630/R-03003F. http://www.epa.gov/ttn/atw/ch ildrens_supplement_final.pdf

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Communication I: Memo from W.H. Farland dated 4 October 2005 to Science Policy

Council. http://www.epa.gov/osa/spc/pdfs/canguid1.pdf

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Chemical Mixtures. EPA-630/R-00-002. http://www.epa.gov/ncea/raf/ pdfs/chem_mix/chem_mix_08_2001.pdf

.

30. US EPA, October 22, 2010. Memorandum from Kaye Whitfield to RTR Wood Furniture Docket, entitled, “Review of Glycol Ether Em issions Associated with the Wood Furniture Manufacturing Source Category”.

31

. US EPA, September 9, 2010. Memorandum Maximum Achievable Control Technology (MACT) Allowable Emissions Estimate ”; Kaye Whitefield Project Lead

32 . US EPA, 2005. Guidelines for Carcinogen Ri sk Assessment (2005). U.S. Environmental Protection Agency, Washington, DC, EPA/630/P-03/001F, 2005. http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=116283

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34 . NRC (National Research Council) 2006. Assessing the Human Health Risks of Trichloroethylene. National Ac ademies Press, Washington DC.

35 . R.P. Subramaniam, P. White and V.J. Cogl iano. 2006. Comparison of cancer slope factors using different st atistical approaches, Risk Anal. Vol 26, p. 825–830.

36. US EPA. 1993. Reference Dose (RfD): Description and Use in Health Risk Assessments. http://www.epa.gov/iris/rfd.htm.

37. US EPA. 1994. Methods for Derivation of Inhalation Reference Concentrations and Application of Inha lation Dosimetry. http://cfpub.epa.gov/ncea/cfm/ recordisplay.cfm?deid=71993

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38. US EPA. 2002. A Review of the Reference Do se and Reference Conc entration Processes. http://www.epa.gov/ncea/iris/RFD_FINAL1.pdf.