From: Higgins, Cortney <Cortney_Higgins@omb.eop.gov> 

Sent: Tuesday, April 15, 2014 12:48 PM 

To: Shine, Brenda 

Cc: Gillis, Thomas; Thundiyil, Karen; Lassiter, Penny 

Subject: RE: Petroleum Refinery RTR--- Interagency Comments under EO12866--- Supporting 

Memoranda 

Hi Brenda, 

Attached are some comments on the Risk Assessment. 

Regards, 

Cortney 

<< RTR_PetRef_EO12866Interagencycomments.pdf >> 
Draft Risk Assessment for the Petroleum Refining Source Sector – 

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Table 2.2-1 AERMOD version 12345 model options for RTR 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) and user-supplied receptors

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 NWS from nearest site ( 824 stations)

In HEM3, meteorological data are ordinarily selected from a list of over 800 National 

Weather Service (NWS) surface observation stations 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. Ideally, when considering off-site 

meteorological data most site specific dispersion modeling efforts will employ up to five 

years of data to capture variability in weather patterns from year to year. However, because 

dispersion model runtimes using five years of meteorological data would be too long for RTR 

source categories with many sources, we modeled only a single year, 2011. 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-prediction at others. For each facility identified by its 

characteristic latitude and longitude coordinates, the closest meteorological station was used 

in the dispersion modeling. The average distance between a modeled facility and the 

applicable meteorological station was 22 miles. Appendix 2 to this document 

(Meteorological Data Processing Using AERMET for HEM3) provides a complete listing of 

stations and assumptions along with further details used in processing the data through 

AERMET, the AERMOD meteorological data processing program. 

Beginning with version 12345, AERMET allows for a minimum wind speed of 0.5 m/s to be 

utilized when processing monitoring stations that are equipped with sonic anemometers and 

for an adjustment to the surface friction velocity (u*). The public version of AERMET 

available at the time we conducted the modeling did not include the surface friction velocity 

adjustment. Also at the time, an updated version of AERMET (and AERMOD) was under 

development that incorporated further surface friction velocity adjustments to AERMET 

version 12345. In anticipation of the update, EPA processed meteorological data using a prepublic release version of AERMET and incorporated the updated surface friction velocity 

adjustment in the output. It was EPA’s judgement that the pre-public release version of 

AERMET would generate AERMOD ready meteorological data very close to that generated 

with the final public release version. 

 

Summary of Comments on Risk and Technology Review 

Assessment – Documentation for ANPRM

Page: 6

Author: OT Subject: Cross-Out Date: 3/27/2014 1:55:32 PM 

Author: OT Subject: Highlight Date: 3/27/2014 2:59:56 PM 

Author: OT Subject: Sticky Note Date: 3/27/2014 1:55:53 PM 

Is there any specific minimum requirement for modeling? 

Author: OT Subject: Highlight Date: 3/27/2014 3:00:36 PM 

Author: OT Subject: Sticky Note Date: 3/27/2014 1:59:56 PM 

What are the factors that explain why a single year model can result in either under- or over-prediction? Can these uncertainties be described briefly? 

Instead of a general statement on under- or over-prediction, can't it be simply stated that single year modeling results in some amount of uncertainty?

Author: OT Subject: Highlight Date: 3/27/2014 3:00:45 PM 

Author: OT Subject: Sticky Note Date: 3/27/2014 2:01:16 PM 

Can the range be given as well?
Draft Risk Assessment for the Petroleum Refining Source Sector – 

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EPA has posted the AERMET meteorological data used in the refinery analysis on the EPA’s 

FERA (Fate, Exposure, and Risk Analysis) website under the HEM model page. AERMET 

and AERMOD (version 13350) were released to the public in late December 2013 on the 

EPA’s Support Center for Regulatory Atmospheric Modeling (SCRAM) website.

 

While it was not feasible to remodel the entire source category using the public released 

version of AERMOD and AERMET, we were able to compare modeling results for 14 

refineries using the using meteorological data processed with the pre-public release version of 

AERMET and data processed with the current 13350 version. In addition, the 13350 version 

of AERMOD was run with the meteorological data processed with the 13350 version of 

AERMET. The comparison was conducted by examining the maximum individual risk 

(MIR) at 14 refinery facilities, including the 4 facilities with the highest risk, the 5 with the 

lowest risks, and 5 randomly selected facilities. The largest difference in MIR values between 

the runs was 3 percent for one facility. The other 13 facilities had differences in their MIR 

values between runs of less than 1 percent. These results of this comparison are presented in 

Appendix 5.

The HEM3 system estimates ambient concentrations at the geographic centroids of census 

blocks (using the 2010 Census) and at other receptor locations that can be specified by the 

user. See Appendix 7 of this document (Dispersion Model Receptor Revisions and Additions 

for the Petroleum Refining Sector) for a discussion of user receptors and centroid location 

changes specific to this source category. The model accounts for the effects of multiple 

facilities when estimating concentration impacts at each block centroid. Typically we 

combined only the impacts of facilities within the same source category, and assessed chronic 

exposure and risk only for census blocks with at least one resident (i.e., locations where 

people may reasonably be 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 finest 

resolution available in the census data, are typically comprised of approximately 40 people or 

about ten households. 

In contrast to the development of ambient concentrations for evaluating long-term exposures, 

which was performed only for occupied census blocks, worst-case short-term (one-hour) 

concentrations were estimated both at the census 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 

where ICR data only contains annual emission totals. 

 

The average hourly emissions rate used for chronic risk modeling is defined as the total 

emissions for a year divided by the total number of operating hours in the year. If a source 

operated 8,760 hours per year this emissions rate would be appropriate for predicting potential 

acute impacts. However, many sources in this source sector emit on a more intermittent basis 

and thus specific hourly emissions may be higher than those utilized for annual or chronic 

Page: 7

Author: OT Subject: Highlight Date: 3/27/2014 3:00:58 PM 

Author: OT Subject: Sticky Note Date: 3/27/2014 2:08:28 PM 

Typically? Not always? Were there situations where the combined impacts of facilities also included those outside the same source category?
Draft Risk Assessment for the Petroleum Refining Source Sector – 

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risks modeling. While hourly emissions data for each source are not available, in this 

assessment, we utilized source category specific engineering judgment to determine what the 

peak hourly emissions for any given process could be. In addition, for past RTR analyses, 

EPA developed a generic emissions adjustment factor of ten for acute screening which was 

based on engineering judgment. This factor of ten and the petroleum refinery specific factors 

are discussed in Appendix 3, (Analysis of data on short-term emission rates relative to longterm emission rates) of this document. 

Census block elevations for HEM3 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 short- and 

long-term ambient concentrations were assumed 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 nearest 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 expected to influence the wind patterns. For details 

on how hill heights were estimated and used in the AERMOD modeling see Appendix 1 of 

this document. 

2.3 Estimating chronic human inhalation exposure 

We used the estimated annual average ambient air concentration of each HAP, at each census 

block centroid, as a surrogate for the lifetime inhalation exposure concentration of all the 

people who reside in the census block. That is, the risk analysis did not consider either the 

short-term or long-term behavior (mobility) of the exposed populations and its potential 

influence on their exposure.

 

We did not address short-term human activity, including indoor air concentrations, for two 

reasons. First, our experience with the NATA assessments (which modeled daily activity 

using EPA’s HAPEM model) suggests that, given our current understanding of 

microenvironment concentrations and daily activities, modeling short-term activity would, on 

average, reduce risk estimates about 25 percent for particulate HAP; it will also reduce risk 

estimates for gaseous HAP, but typically by much less. Second, basing 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 behavior 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 assumption 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 highest concentration where 

Page: 8

Author: OT Subject: Highlight Date: 3/27/2014 3:01:58 PM 

Author: OT Subject: Sticky Note Date: 3/27/2014 3:03:34 PM 

The technical memorandum in Appendix 3 provides specific emissions adjustment factors for different structures. Can these be used?

Author: OT Subject: Sticky Note Date: 3/27/2014 3:10:06 PM 

Above, it was stated that your experience with modeling short-term activity would reduce risk estimates by 25% for particulate HAP, and also some amount for 

gaseous HAP. Is there a reason for not using these factors if they are known rather than simply accounting for them as adding systematic low bias?

Author: OT Subject: Highlight Date: 3/27/2014 3:07:28 PM 
Draft Risk Assessment for the Petroleum Refining Source Sector – 

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people are likely to live; the population risk distribution; and 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 population number remains the same. It does, however, affect the shape of the 

distribution of individual risks across the affected population, shifting it toward higher 

estimated individual risks at the upper end and reducing the number of people estimated to be 

at lower risks, thereby increasing the estimated number of people at specific risk levels. 

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. This was generally the Fenceline.

2.4 Acute Risk Screening and Refined Assessments 

In establishing a scientifically defensible approach for the assessment of potential health risks 

due to acute exposures to HAP, we followed the same general approach that has been used for 

developing chronic health risk assessments under the residual risk program. That is, we 

developed a tiered, iterative approach. This approach to risk assessment was endorsed by the 

National Academy of Sciences in its 1993 publication “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 eliminate 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 tiered, iterative approach to the assessment. This 

means that we began with a screening assessment, which relies on readily available data and 

uses conservative assumptions that in combination approximate a worst-case exposure. The 

result of this screening process is that either the facility being assessed poses no potential 

acute health risks (i.e., it “screens out”), or that it requires further, more refined assessment. 

A more refined acute assessment could use industry- or site-specific data on the temporal 

pattern of emissions, the layout of emission points at the facility, the boundaries of the 

facility, and/or the local meteorology. In some cases, all of these site-specific data are used to 

refine the assessment; in others, lesser amounts of site-specific data can be used to determine 

that acute exposures are not a concern, and significant additional data collection is not 

necessary. 

Acute health risk screening was performed for each facility as the first step. We used 

conservative assumptions for emission rates, meteorology, and exposure location. We used 

the following worst-case assumptions in our screening approach:

• Peak 1-hour emissions were obtained from the ICR and based on the operating 

characteristics of refinery emission sources as described in Appendix 3. 

• We assumed that the peak emissions 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 

Page: 9

Author: OT Subject: Highlight Date: 3/27/2014 3:16:01 PM 

Author: OT Subject: Sticky Note Date: 3/27/2014 3:17:26 PM 

It doesn't bias the MIR because it already assumes the worst case scenario (70 year exposure)?
Draft Risk Assessment for the Petroleum Refining Source Sector – 

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example, via consumption of water from untreated local sources or ingestion of locally grown 

food. Nevertheless, the Administrator judged that such a standard would protect, with an 

adequate margin of safety, the health of children and other at-risk populations against an array

of adverse health effects, most notably including neurological effects, particularly 

neurobehavioral and neurocognitive effects, in children (73 FR 67007). The Administrator, in 

setting the standard, also recognized that no evidence-or risk based bright line indicated a 

single appropriate level. Instead a collection of scientific evidence and other information was 

used to select the standard from a range of reasonable values (73 FR 67006).

We further note that comparing ambient lead concentrations to the NAAQS for lead, 

considering the level, averaging time, form and indicator, also informs whether there is the 

potential for adverse environmental effects. This is because the secondary lead NAAQS, set 

to protect against adverse welfare effects (including adverse environmental effects), has the 

same averaging time, form, and level as the primary standard. Thus, ambient lead 

concentrations above the NAAQS for lead also indicate the potential for adverse 

environmental effects.

Adverse environmental effects

The EPA has developed a screening approach to examine the potential for adverse 

environmental effects as required under section 112(f)(2)(A) of the CAA. The environmental 

screen focuses on the following seven environmental HAP: 

• Five persistent bioaccumulative HAP (PB-HAP) – cadmium, dioxins/furans, 

polycyclic organic matter (POM), mercury (both inorganic mercury and 

methyl mercury) and lead.

• Two acid gases - hydrogen chloride (HCl), and hydrogen fluoride (HF). 

HAP that persist and bioaccumulate are of particular environmental concern because they 

accumulate in the soil, sediment and water. The acid gases, HCl and HF, were included due to 

their well-documented potential to cause direct damage to terrestrial plants.

For the environmental risk screening analysis, EPA first determined whether any facilities in 

the source category emitted any of the seven environmental HAP. If one or more of the seven 

environmental HAP evaluated are emitted by at least one facility in the source category we 

proceed to the second step of the evaluation.

For cadmium, mercury, POM and dioxins/furans, the environmental screening analysis 

consists of two tiers. In the first tier, TRIM.FaTE modeling was conducted under worst-cast 

environmental conditionals to determine whether the facility-specific off-site emission rates 

of each of the emitted environmental HAP exceeded ecological benchmarks represented as 

emissions threshold values. If off-site emissions from a facility do not exceed the Tier I

thresholds the facility “passes” the screen, and therefore, is not evaluated further under the 

screening approach. If off-site emissions from a facility exceed the Tier I thresholds, we 

evaluate the facility further in Tier II. 

Page: 12

Author: OT Subject: Highlight Date: 3/28/2014 12:50:56 PM 

Author: OT Subject: Sticky Note Date: 3/28/2014 12:51:09 PM 

References?

Author: OT Subject: Sticky Note Date: 3/28/2014 12:54:49 PM 

Are these actual values derived for air, or are they derived from values for non-air media?

Author: OT Subject: Highlight Date: 3/28/2014 12:53:09 PM 
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In Tier II of the environmental screening analysis, the screening emission thresholds are 

adjusted to account for local meteorology and the actual location of lakes in the vicinity of 

facilities that did not pass the Tier I screen. If off-site emissions from a facility do not exceed 

the Tier II thresholds, the facility passes the screen, and is typically not evaluated further. If 

off-site emissions from a facility exceed the Tier II thresholds, the facility does not pass the 

screen and, therefore, may have the potential to cause adverse environmental effects. Such 

facilities are evaluated further to investigate factors such as the magnitude and characteristics 

of the area of exceedance. 

For acid gases, the environmental screening analysis evaluates the potential phytotoxicity and 

reduced productivity of plants due to chronic exposure to acid gases. The environmental risk 

screening methodology for acid gases is a single-tier screen that compares the average off-site 

ambient air concentration over the modeling domain to ecological benchmarks for each of the 

acid gases. For purposes of ecological risk screening, EPA identifies a potential for adverse 

environmental effects to plant communities from exposure to acid gases when the average 

concentration of the HAP around a facility exceeds the LOAEL ecological benchmark. In 

such cases, we further investigate factors such as the magnitude and characteristics of the area 

of exceedance (e.g., land use of exceedance area, size of exceedance area) to determine if 

there is an adverse environmental effect. 

For lead compounds, we currently do not have the ability to calculate media concentrations 

using the TRIM.FaTE model. However, air concentrations of lead are already calculated as 

part of the human health exposure and risk analysis using the HEM-3 AERMOD air 

dispersion model. To evaluate the potential for adverse environmental effects from lead, we 

compare the average modeled air emission concentrations of lead from each facility in the 

source category emissions to the level of the secondary National Ambient Air Quality 

Standard (NAAQS) for lead. The secondary lead NAAQS is a reasonable means of evaluating 

environmental risk because it is set to provide substantial protection against adverse welfare 

effects which can include “effects on soils, water, crops, vegetation, man-made materials, 

animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and 

hazards to transportation, as well as effects on economic values and on personal comfort and 

well-being.”2

2.6 Dose-response assessment

2.6.1 Sources of chronic dose-response information 

Dose-response assessment (carcinogenic and non-carcinogenic) for chronic exposure (either 

by inhalation or ingestion) for the HAP reported in the emissions inventory for the petroleum 

 

2 A secondary standard, as defined in Section 109(b)(2), must “specify a level of air quality the attainment and 

maintenance of which, in the judgment of the Administrator, based on criteria, is requisite to protect the public 

welfare from any known or anticipated adverse effects associated with the presence of [the] pollutant in the 

ambient air.” Welfare effects as defined in section 302(h) (42 U.S.C. 7602(h)) include, but are not limited to, 

“effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility and climate, 

damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and 

on personal comfort and well-being.”

Page: 13

Author: OT Subject: Sticky Note Date: 3/28/2014 12:59:29 PM 

Are the ecological benchmarks presented in this document and appendices?

Author: OT Subject: Highlight Date: 3/28/2014 12:58:53 PM 
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summarized in a review by Grimsrud et al., 2010 [22]). The major scientific bodies 

mentioned above have also recognized that there are differences in toxicity and/or 

carcinogenic potential across the different nickel compounds. In this inhalation risk 

assessment, to take a conservative approach, we have considered all nickel compounds to 

be as carcinogenic as nickel subsulfide and have applied the IRIS URE for nickel 

subsulfide without a factor to reflect the assumption that 100 percent of the total mass of 

nickel may be as carcinogenic as pure nickel subsulfide. In addition, given that there are 

two additional URE values3 derived for exposure to mixtures of nickel compounds, as a 

group, that are 2-3 fold lower than the IRIS URE for nickel subsulfide, the EPA also 

considers it reasonable to use a value that is 50 percent of the IRIS URE for nickel 

subsulfide for providing an estimate of the lower end of the plausible range of cancer 

potency values for different mixtures of nickel compounds. 

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 develop a composite dose-response value for the category. 

3) Where POM emissions were not speciated into individual compounds, we applied the 

same simplifying assumptions to assessments that are used in NATA [23]. 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 which we can apply the same URE.

4) In the case of HAP categories such as glycol ethers and cyanide compounds, the most 

conservative dose-response value of the chemical 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 HAP that lack doseresponse values may pose an unacceptable risk and require further examination, or screen 

out from further assessment. 

5) In 2004, the EPA determined that the Chemical Industry Institute of Toxicology (CIIT) 

cancer dose-response value for formaldehyde (5.5 x 10-9 per μg/m3

) was based on better 

science than the IRIS cancer dose-response value (1.3 x 10-5 per μg/m3

), and we switched 

from using the IRIS value to the CIIT value in risk assessments supporting regulatory 

actions. Subsequent research published by EPA suggests that the CIIT model was not 

appropriate and, in 2010, EPA returned to using the 1991 IRIS value. The EPA has been 

working on revising the formaldehyde IRIS assessment and the National Academy of 

Sciences (NAS) completed its review of the EPA’s draft assessment in April of 20114 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 currently presented in the External Review 

 

3

 Two UREs (other than the current IRIS values) have been derived for nickel compounds as a group: one 

developed by the California Department of Health Services 

(http://www.arb.ca.gov/toxics/id/summary/nickel_tech_b.pdf) and the other by the Texas Commission on 

Environmental Quality (http://www.epa.gov/ttn/atw/nata1999/99pdfs/healtheffectsinfo.pdf). 4

 http://www.nap.edu/catalog.php?record_id=13142 

Page: 16

Author: OT Subject: Highlight Date: 3/28/2014 1:30:44 PM 

Author: OT Subject: Highlight Date: 3/28/2014 1:31:16 PM 

Author: OT Subject: Sticky Note Date: 3/28/2014 1:34:20 PM 

Is the range presented? It is overly conservative if no factor is used, given that 35% of nickel emissions may be soluble, and 65% are carcinogenic.
Draft Risk Assessment for the Petroleum Refining Source Sector – 

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17

draft of the assessment and will discuss their strengths 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 estimate; EPA may also consider other information 

as the science evolves. 

6) A chronic screening level of 163 μg/m3 was developed by EPA for carbonyl sulfide 

(COS) from a No Observed Adverse Effects Level (NOAEL) of 200 ppm based on brain 

lesions and neurophysiological alterations in rodents. A more detailed discussion of the 

studies used to develop the COS chronic screening level is provided in Appendix 8 of this 

document (Development of the Chronic Screening Level for Carbonyl Sulfide). The 

screening level includes a total uncertainty factor (UF) of 3,000: 10 times for 

extrapolation for interspecies differences, 10 times for consideration of intraspecies 

variability, 10 times for extrapolation from subchronic to chronic duration, and 3 times for 

database insufficiencies. See Section 5 of this document for a detailed discussion of 

exposure modeling uncertainties. 

The emissions inventory for the petroleum refinery source sector includes emissions of 135 

HAP with available chronic quantitative inhalation dose-response values. Of these, 69 are 

classified as known, probably, or possible carcinogens, with quantitative cancer dose-response 

values available. These 69 HAP, their quantitative inhalation chronic cancer dose-response 

values, and the source of each value are listed in Table 2.6-1.A. The 14 compounds with 

chronic oral cancer dose-response values available (for which multipathway screening 

assessments were performed) are listed in Table 2.6-1.B. Ninety-five HAP have quantitative 

inhalation chronic noncancer threshold values available; 7 of these 95 HAP, for which a 

multipathway assessment was performed, also have quantitative oral chronic noncancer 

threshold values available. These 95 HAP, their threshold values, and the source of the value 

are listed in Table 2.6-2.A and Table 2.6-2.B. 

Table 2.6-1 (A) Dose-Response Values for 

Chronic Inhalation Exposure to Carcinogens

URE (unit risk estimate for cancer)5 = cancer risk per μg/m3 of average lifetime exposure. 

Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research & 

Development, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California 

EPA Office of Environmental Health Hazard Assessment. HEAST = EPA Health Effects 

Assessment Tables.

Pollutant CAS Number6 URE5

(1/μg/m3) 

Source

1,1,2,2-Tetrachloroethane 79345 5.8E-05 IRIS

1,1,2-Trichloroethane 79005 1.6E-05 IRIS

1,3-Butadiene 106990 3.0E-05 IRIS

1,3-Dichloropropene 542756 4.0E-06 IRIS

 

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 μg/m3 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 HAP.

Page: 17

Author: OT Subject: Sticky Note Date: 3/28/2014 1:36:43 PM 

This chronic screening level wasn't peer-reviewed? 

Author: OT Subject: Highlight Date: 3/28/2014 1:36:07 PM