[Federal Register: August 24, 2009 (Volume 74, Number 162)]
[Notices]
[Page 42659-42660]
[DOCID:fr24au09-45]

-----------------------------------------------------------------------

DEPARTMENT OF ENERGY

Federal Energy Regulatory Commission

[Project No. 13554-000]

Free Flow Power Ohio River 17, LLC; Notice of Preliminary Permit
Application Accepted for Filing and Soliciting Comments, Motions To
Intervene, and Competing Applications

August 14, 2009.
    On July 14, 2009, Free Flow Power Ohio River 17, LLC filed an
application for a preliminary permit, pursuant to section 4(f) of the
Federal Power Act, proposing to study the feasibility of the Stouts
Pass Hydrokinetic Energy Project, located on the Atchafalaya River,
Saint Mary Parish, Louisiana. The sole purpose of a preliminary permit,
if issued, is to grant the permit holder priority to file a license
application during the permit term. A preliminary permit does not
authorize the permit holder to perform any land disturbing activities
or otherwise enter upon lands or waters owned by others without the
owners' express permission.
    The proposed project would consist of: (1) 720 turbine-generator
units configured in a series of turbine arrays which in turn will be
grouped to form turbine fields; (2) a combination of freestanding
pilings, a floating barge-like platform, or existing shore
infrastructure such as dock pilings onto which the turbine arrays will
be moored; (3) submersible electric cables interconnecting the arrays
within each turbine field and transmitting the turbine field's
generation to a shore station; (4) several shore stations each
consisting of less than 100 square meters which will transition the
submersible cabling to the overhead transmission; (5) a 2.6-mile, 69 kV
line interconnecting the shore stations and delivering power to the
project substation; and (6) appurtenant

facilities. The proposed project would generate about 63 gigawatt-hours
annually.
    Applicant contact: Ramya Swaminathan, Free Flow Power Corporation,
33 Commercial Street, Gloucester, Massachusetts 01930, phone: (978)
226-1531.
    FERC Contact: Sergiu Serban,<PHONE_NUMBER>.
    Deadline for filing comments, motions to intervene, competing
applications (without notices of intent), or notices of intent to file
competing applications: 60 days from the issuance of this notice.
Comments, motions to intervene, notices of intent, and competing
applications may be filed electronically via the Internet. See 18 CFR
385.2001(a)(1)(iii) and the instructions on the Commission's Web site
under the ``e-Filing'' link. If unable to be filed electronically,
documents may be paper-filed. To paper-file, an original and eight
copies should be mailed to: Kimberly D. Bose, Secretary, Federal Energy
Regulatory Commission, 888 First Street, NE., Washington, DC 20426. For
more information on how to submit these types of filings please go to
the Commission's Web site located at http://www.ferc.gov/filing-
comments.asp. More information about this project, including a copy of
the application, can be viewed or printed on the ``eLibrary'' link of
Commission's Web site at http://www.ferc.gov/docs-filing/elibrary.asp.
Enter the docket number (P-13554) in the docket number field to access
the document. For assistance, call toll-free 1-866-208-3372.

Kimberly D. Bose,
Secretary.
[FR Doc. E9-20233 Filed 8-21-09; 8:45 am]

BILLING CODE 6717-01-P


[Federal Register Volume 83, Number 125 (Thursday, June 28, 2018)]
[Rules and Regulations]
[Pages 30338-30340]
[FR Doc No: 2018-13912]

=======================================================================
-----------------------------------------------------------------------

DEPARTMENT OF HOMELAND SECURITY

Coast Guard

33 CFR Part 100

[Docket Number USCG-2018-0316]
RIN 1625-AA08

Special Local Regulation; Gulf of Mexico; Sarasota, FL

AGENCY: Coast Guard, DHS.

ACTION: Temporary final rule.

-----------------------------------------------------------------------

SUMMARY: The Coast Guard is establishing a special local regulation on
the waters of the Gulf of Mexico, in the vicinity of Lido Beach,
Florida, during the 34th Annual Sarasota Powerboat Grand Prix High
Speed Boat Race. Approximately 35 boats and jet skis, traveling at
speeds in excess of 100 miles per hour are expected to participate.
Additionally, it is anticipated that 300 spectator vessels will be
present along the race course. The special local regulation is
necessary to protect the safety of race participants, participant
vessels, spectators, and the general public on navigable waters of the
United States during the event. The special local regulation will
establish an enforcement area where all persons and vessels, except
those persons and vessels participating in the high speed boat races,
are prohibited from entering, transiting through, anchoring in, or
remaining within without obtaining permission from the Captain of the
Port St. Petersburg or a designated representative.

DATES: This rule is effective daily from 8 a.m. until 6 p.m. on June
29, 2018 through July 1, 2018.

ADDRESSES: To view documents mentioned in this preamble as being
available in the docket, go to http://www.regulations.gov, type USCG-
2018-0316 in the ``SEARCH'' box and click ``SEARCH.'' Click on Open
Docket Folder on the line associated with this rule.

FOR FURTHER INFORMATION CONTACT: If you have questions on this rule,
call or email Marine Science Technician First Class Michael
Shackleford, Sector St. Petersburg Prevention Department, Coast Guard;
telephone<PHONE_NUMBER>, email [email protected].

SUPPLEMENTARY INFORMATION:

I. Table of Abbreviations

CFR Code of Federal Regulations
DHS Department of Homeland Security
FR Federal Register
NPRM Notice of proposed rulemaking
Pub. L. Public Law
Sec.  Section
U.S.C. United States Code
COTP Captain of the Port

II. Background Information and Regulatory History

    The Coast Guard is establishing this special local regulation
without prior notice and opportunity to comment pursuant to authority
under section 4(a) of the Administrative Procedure Act (APA) (5 U.S.C.
553(b)). This provision authorizes an agency to issue a rule without
prior notice and opportunity to comment when the agency for good cause
finds that those procedures are ``impracticable, unnecessary, or
contrary to the public interest.'' Under 5 U.S.C. 553(b)(B), the Coast
Guard finds that good cause exists for not publishing a notice of
proposed rulemaking (NPRM) with respect to this rule because it is
impracticable. Insufficient time remains to publish an NPRM and to
receive public comments, as the Sarasota Powerboat Grand Prix event
will occur before the rulemaking process would be completed. Because of
the dangers associated with high speed boat races, the regulation is
necessary to provide for the safety of event participants, spectators,
and vessels transiting the event area. For those reasons, it would be
impracticable to publish an NPRM.
    Under 5 U.S.C. 553(d)(3), the Coast Guard finds that good cause
exists for making this rule effective less than 30 days after
publication in the Federal Register. For the reason discussed above,
the Coast Guard finds that good cause exists.

III. Legal Authority and Need for Rule

    The legal basis for this rule is the Coast Guard's authority to
establish special local regulations: 33 U.S.C. 1233. The purpose of the
rule is to provide for the safety of life on navigable waters of the
United States during the Sarasota Powerboat Grand Prix High Speed Boat
Race.

IV. Discussion of the Rule

    This rule establishes a special local regulation that will
encompass certain waters of the Gulf of Mexico, Lido Beach, Florida.
The special local regulation will be enforced daily from 8 a.m. to 6
p.m. on June 29, 2018 through July 1, 2018. The special local
regulation will establish an enforcement area where all persons and
vessels, except those persons and vessels participating in the high
speed boat races, are prohibited from entering, transiting through,
anchoring in, or remaining within without obtaining permission from the
COTP St. Petersburg or a designated representative.
    Persons and vessels may request authorization to enter, transit
through, anchor in, or remain within the regulated area by contacting
the Captain of the Port (COTP) St. Petersburg by telephone at (727)
824-7506, or a designated representative via VHF radio on channel 16.
If authorization to enter, transit through, anchor in, or remain within
the regulated area is granted by the COTP St. Petersburg or a
designated representative, all persons and vessels receiving such
authorization must comply with the instructions of the COTP St.
Petersburg or a designated representative. The Coast Guard will provide
notice of the special local regulation by Local Notice to Mariners and/
or Broadcast Notice to Mariners.

V. Regulatory Analyses

    We developed this rule after considering numerous statutes and
Executive orders related to rulemaking. Below we summarize our analyses
based on a number of these statutes and executive orders, and we
discuss First Amendment rights of protestors.

A. Regulatory Planning and Review

    Executive Orders 12866 and 13563 direct agencies to assess the
costs and benefits of available regulatory alternatives and, if
regulation is necessary, to select regulatory approaches that maximize
net benefits. Executive Order 13771 directs agencies to control
regulatory costs through a budgeting process. This rule has not been
designated a ``significant regulatory action,'' under Executive Order
12866. Accordingly, this rule has not been reviewed by the Office of
Management and Budget (OMB), and pursuant to OMB guidance it is exempt
from the requirements of Executive Order 13771.
    This regulatory action determination is based on: (1) The special
local regulation will be enforced for only ten hours on two days; (2)
although persons and vessels may not enter, transit through, anchor in,
or remain within the regulated area without authorization from the COTP
St. Petersburg or a designated representative, they may operate in the
surrounding area during the enforcement period; (3) persons and vessels
may still enter, transit through, anchor in, or remain within the
regulated area or anchor in the sponsor's designated spectator area(s),
during the enforcement period if authorized by the COTP St. Petersburg
or a designated representative; and (4) the Coast Guard will provide
advance notification of the special local regulation to the local
maritime community by Local Notice to Mariners and/or Broadcast Notice
to Mariners.

B. Impact on Small Entities

    The Regulatory Flexibility Act of 1980, 5 U.S.C. 601-612, as
amended, requires federal agencies to consider the potential impact of
regulations on small entities during rulemaking. The term ``small
entities'' comprises small businesses, not-for-profit organizations
that are independently owned and operated and are not dominant in their
fields, and governmental jurisdictions with populations of less than
50,000. The Coast Guard certifies under 5 U.S.C. 605(b) that this rule
would not have a significant economic impact on a substantial number of
small entities.
    While some owners or operators of vessels intending to transit the
safety zone may be small entities, for the reasons stated in section
V.A above, this rule will not have a significant economic impact on any
vessel owner or operator.
    Under section 213(a) of the Small Business Regulatory Enforcement
Fairness Act of 1996 (Pub. L. 104-121), we want to assist small
entities in understanding this rule. If the rule would affect your
small business, organization, or governmental jurisdiction and you have
questions concerning its provisions or options for compliance, please
contact the person listed in the FOR FURTHER INFORMATION CONTACT
section.
    Small businesses may send comments on the actions of federal
employees who enforce, or otherwise determine compliance with, federal
regulations to

the Small Business and Agriculture Regulatory Enforcement Ombudsman and
the Regional Small Business Regulatory Fairness Boards. The Ombudsman
evaluates these actions annually and rates each agency's responsiveness
to small business. If you wish to comment on actions by employees of
the Coast Guard, call 1-888-REG-FAIR (1-888-734-3247). The Coast Guard
will not retaliate against small entities that question or complain
about this rule or any policy or action of the Coast Guard.

C. Collection of Information

    This rule will not call for a new collection of information under
the Paperwork Reduction Act of 1995 (44 U.S.C. 3501-3520).

D. Federalism and Indian Tribal Governments

    A rule has implications for federalism under Executive Order 13132,
Federalism, if it has a substantial direct effect on the States, on the
relationship between the national government and the States, or on the
distribution of power and responsibilities among the various levels of
government. We have analyzed this rule under that Order and have
determined that it is consistent with the fundamental federalism
principles and preemption requirements described in Executive Order
13132.
    Also, this rule does not have tribal implications under Executive
Order 13175, Consultation and Coordination with Indian Tribal
Governments, because it would not have a substantial direct effect on
one or more Indian tribes, on the relationship between the Federal
Government and Indian tribes, or on the distribution of power and
responsibilities between the Federal Government and Indian tribes. If
you believe this rule has implications for federalism or Indian tribes,
please contact the person listed in the FOR FURTHER INFORMATION CONTACT
section above.

E. Unfunded Mandates Reform Act

    The Unfunded Mandates Reform Act of 1995 (2 U.S.C. 1531-1538)
requires federal agencies to assess the effects of their discretionary
regulatory actions. In particular, the Act addresses actions that may
result in the expenditure by a State, local, or tribal government, in
the aggregate, or by the private sector of $100,000,000 (adjusted for
inflation) or more in any one year. Though this rule would not result
in such expenditure, we do discuss the effects of this rule elsewhere
in this preamble.

F. Environment

    We have analyzed this rule under Department of Homeland Security
Management Directive 023-01 and Commandant Instruction M16475.lD, which
guide the Coast Guard in complying with the National Environmental
Policy Act of 1969 (42 U.S.C. 4321-4370f), and have made a
determination that this action is one of a category of actions that do
not individually or cumulatively have a significant effect on the human
environment. This rule involves a special local regulation issued in
conjunction with a regatta or marine parade. It is categorically
excluded from further review under paragraph L61 of Appendix A, Table 1
of DHS Instruction Manual 023-01-001-01, Rev. 01. A Record of
Environmental Consideration supporting this determination is available
in the docket where indicated under ADDRESSES.

G. Protest Activities

    The Coast Guard respects the First Amendment rights of protesters.
Protesters are asked to contact the person listed in the FOR FURTHER
INFORMATION CONTACT section to coordinate protest activities so that
your message can be received without jeopardizing the safety or
security of people, places, or vessels.

List of Subjects in 33 CFR Part 100

    Marine safety, Navigation (water), Reporting and recordkeeping
requirements, Waterways.

    For the reasons discussed in the preamble, the Coast Guard amends
33 CFR part 100 as follows:

PART 100--SAFETY OF LIFE ON NAVIGABLE WATERS

0
1. The authority citation for part 100 continues to read as follows:

    Authority:  33 U.S.C. 1233; 33 CFR 1.05-1.

0
2. Add Sec.  100.35T07-0316 to read as follows:

Sec.  100.35T07-0316  Special Local Regulations; Sarasota Powerboat
Grand Prix, Gulf of Mexico; Lido Beach, FL.

    (a) Location. The following regulated area is established as a
special local regulation. All coordinates are North American Datum
1983.
    (1) Enforcement area. All waters of the Gulf of Mexico contained
within the following points: 27[deg]18'44'' N, 82[deg]36'14'' W, thence
to position 27[deg]19'09'' N, 82[deg]35'13'' W, thence to position
27[deg]17'42'' N, 82[deg]34'00'' W, thence to position 27[deg]16'43''
N, 82[deg]34'49'' W, thence back to the original position,
27[deg]18'44'' N, 82[deg]36'14'' W.
    (b) Definition. The term ``designated representative'' means Coast
Guard Patrol Commanders, including Coast Guard coxswains, petty
officers, and other officers operating Coast Guard vessels, and
Federal, state, and local officers designated by or assisting the COTP
St. Petersburg in the enforcement of the regulated areas.
    (c) Regulations. (1) All non-participant persons and vessels are
prohibited from entering, transiting through, anchoring in, or
remaining within the race area unless an authorized by the COTP St.
Petersburg or a designated representative.
    (2) Designated representatives may control vessel traffic
throughout the enforcement area as determined by the prevailing
conditions.
    (3) Persons and vessels may request authorization to enter, transit
through, anchor in, or remain within the regulated areas by contacting
the COTP St. Petersburg by telephone at<PHONE_NUMBER>, or a designated
representative via VHF radio on channel 16. If authorization is
granted, all persons and vessels receiving such authorization must
comply with the instructions of the COTP St. Petersburg or a designated
representative.
    (4) The Coast Guard will provide notice of the regulated area by
Local Notice to Mariners and/or Broadcast Notice to Mariners.
    (d) Enforcement period. This rule will be enforced daily from 8
a.m. to 6 p.m. on June 29, 2018 through July 1, 2018.

H.L. Najarian,
Captain, U.S. Coast Guard, Captain of the Port Saint Petersburg.
[FR Doc. 2018-13912 Filed 6-27-18; 8:45 am]
 BILLING CODE 9110-04-P


Worst-case acute HQs were calculated for every HAP that has an acute dose-response value, as shown in Table 3.1-1. For cases where the screening HQ was greater than 1, we further refined the estimates by determining the highest HQ value that might occur outside facility boundaries. Based on actual baseline emissions, the highest refined screening acute HQ value of 4 (based on the acute REL for formaldehyde) is shown in Table 3.2-2. This value includes a refinement of determining the highest HQ value that is outside facility boundaries. It is also important to note that the highest HQ assumes that the primary source of the formaldehyde emissions from asphalt storage tanks was modeled with an hourly emissions multiplier of 64 to 96 times (depending on the emission source and facility) the annual emissions rate. No facilities are estimated to have a hazard quotient based on an AEGL or an EPRG greater than 1. Acute estimates for each plant and pollutant are provided in Appendix 10 of this document.

Table 3.2-2.  Source Categories Level Inhalation Risks for Asphalt Processing and Asphalt Roofing Manufacturing Based on Actual and Allowable Emissions

                                    Result
                               HAP "Drivers"
Facilities in Source Categories
Number of Facilities Estimated to be in Source Categories
                                       8
                                      n/a
Number of Facilities Modeled in Risk Assessment
                                       8
                                      n/a
Cancer Risks
Maximum Individual Lifetime Cancer Risk (in 1 million)
                                    < 1
                                      n/a
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
                                       0
                                      n/a
                                        Greater than or equal to 1-in-1 million
                                       0
                                      n/a
Chronic Noncancer Risks
Maximum Respiratory Hazard Index
                                      0.1
                                   acrolein
Number of Facilities with Maximum Respiratory Hazard Index:
	Greater than 1
                                       0
                                      n/a
Acute Noncancer Screening Results
                                                  Maximum Acute Hazard Quotient
                                       4
                              formaldehyde (REL)
                          Number of Facilities With Potential for Acute Effects
                                       2
                            formaldehyde, acrolein
Population Exposure
             Number of People Living Within 50 Kilometers of Facilities Modeled
                                   7,000,000
                                      n/a
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
                                       0
                                      n/a
                                       	Greater than or equal to 1-in-1 million
                                       0
                                      n/a
Number of People Exposed to Noncancer Respiratory Hazard Index:
                                                                	Greater than 1
                                       0
                                      n/a
Estimated Cancer Incidence (excess cancer cases per year)
                                    0.0007
                                      n/a
Contribution of HAP to Cancer Incidence
                                                                  	formaldehyde
                                      48%
                                      n/a
                                                                 	1,3-butadiene
                                      10%
                                      n/a
                                                              	nickel compounds
                                      8%
                                      n/a
                                                                       	benzene
                                      7%
                                      n/a
                                                                       	cadmium
                                      6%
                                      n/a
                                                              arsenic compounds
                                      5%
                                      n/a
                                                 7,12-dimethylbenz[a]anthracene
                                      5%
                                      n/a
                                                                  ethyl benzene
                                      4%
                                      n/a
                                                        chromium (VI) compounds
                                      3%
                                      n/a

Facility-wide Inhalation
The facility-wide chronic MIR and TOSHI, available in Appendix 10, are based on emissions from all sources at the identified facilities (both MACT and non-MACT sources). The results of the facility-wide assessment for cancer risks, as compared to the source categories' assessment, are summarized in Table 3.2-3. The results indicate that 4 facilities have a facility-wide cancer MIR greater than or equal to 1-in-1 million. The maximum facility-wide cancer MIR is 9-in-1 million, mainly driven by naphthalene and benzene emissions from facility wide fugitive emissions and nickel compound emissions from flares from the petroleum refinery source category. The total estimated cancer incidence from the whole facility is 0.001 excess cancer cases per year, or one excess case in every 715 years. Approximately 6,000 people were estimated to have cancer risks above 1-in-1 million from exposure to HAP emitted from both MACT and non-MACT sources at the 8 facilities in these source categories. The maximum facility-wide TOSHI for the source categories is estimated to be 0.1, which occurred at 2 facilities. At one facility, it was driven by non-source categories' (petroleum refinery) emissions of naphthalene from facility wide fugitive emissions, nickel compounds from flares, and acrolein emissions from process heaters. At the other facility, it was driven by source categories' emissions of acrolein from blowing stills. No people are exposed to noncancer hazard index levels above 1, based on facility-wide emissions from the 8 facilities in these source categories.

Table 3.2-3.  Source Categories Contribution to Facility-Wide Cancer Risks Based on Actual Emissions

              Asphalt Processing and Asphalt Roofing Manufacturing
        Number of Facilities Binned by Facility-Wide MIR (in 1 million)
            Source Categories MIR Contribution to Facility-Wide MIR
                                     <1
 1<= MIR<10
10<= MIR<100
                                   > 100
                                     Total
                                   > 90%
                                       2
                                       0
                                       0
                                       0
                                       2
                                    50-90%
                                       1
                                       0
                                       0
                                       0
                                       1
                                    10-50%
                                       1
                                       1
                                       0
                                       0
                                       2
                                   < 10%
                                       0
                                       3
                                       0
                                       0
                                       3
Total
                                       4
                                       4
                                       0
                                       0
                                       8

Multipathway
Table 3.2-4 summarizes the multipathway risk results for these source categories based on baseline actual emissions. The PB-HAP emitted by facilities in these source categories include POM (of which PAH is a subset), lead compounds, cadmium compounds, mercury compounds, and arsenic. To identify potential multipathway health risks from PB-HAP other than lead, we first performed a tiered screening assessment (Tiers 1, 2, and 3) based on emissions of PB-HAP emitted from each facility in the source categories (see section 2.5).

Of the 8 facilities in the source categories, 3 facilities reported emissions of carcinogenic PB-HAP (arsenic and POM) and 1 facility reported emissions of non-carcinogenic PB-HAP (cadmium and mercury) that exceed the Tier 1 screening value of 1. The maximum arsenic screening result exceeded the screening value by a factor of 5 while the maximum POM screening result exceeded the screening value by a factor of 16. For the non-carcinogens, the maximum screening result for both mercury and cadmium exceeded the screening value by a factor of 2. Due to the theoretical construct of the screening model, these factors are not directly translatable into estimates of risk or hazard quotients for these facilities; rather they indicate that the initial multipathway screening assessment does not rule out the potential for multipathway impacts of concern. Table 3.2-4 summarizes the results of the Tier 1 screening assessment.

For the PB-HAP and facilities that exceeded the Tier 1 multipathway screening value, we used facility site-specific information to refine some of the assumptions associated with the local area around the facilities. While maintaining the exposure assumptions, we refine the scenario to examine a subsistence fisher and a gardener separately to develop a Tier 2 screening value. (See Section 2.5 and Appendix 6 of this document for more information on the Tier 2 screening assessment.) The additional site-specific information included the land use around the facilities, the location of fishable lakes, and local wind direction and speed. The result of this assessment was the development of site-specific emission screening values for each of the PB-HAP. Based on this Tier 2 screening assessment, the arsenic, cadmium, and mercury emission rates for all facilities were below levels of concern. POM emissions exceeded the screening value by a factor of 2 for the fisher scenario but were below levels of concern for the gardener scenario. Table 3.2-4 summarizes the results of the Tier 2 screening assessment.

An exceedance of a screening value in any of the tiers cannot be equated with a risk value or a hazard quotient (or hazard index). Rather, it represents a high-end estimate of what the risk or hazard may be. For example, facility emissions exceeding the screening value by a factor of 2 for a non-carcinogen can be interpreted to mean that we are confident that the HQ would be lower than 2. Similarly, facility emissions exceeding the screening value by a factor of 30 for a carcinogen means that we are confident that the risk is lower than 30-in-1 million. Our confidence comes from the conservative, or health-protective, assumptions encompassed in the screening tiers: we choose inputs from the upper end of the range of possible values for the influential parameters used in the screening tiers; and we assume that the exposed individual exhibits ingestion behavior that would lead to a high total exposure.

Table 3.2-4.  Source Categories Level Multipathway Screening Assessment Risk Results for the Asphalt Processing and Asphalt Roofing Manufacturing

 Tiered Multipathway Maximum Screening Values (SV) (# facilities above SV = 1)

                                    Tier 1
                                    Tier 2
                                    Tier 3

                                    PB-HAP
                          Facilities Emitting PB-HAP

                               Fisher and Farmer

                                    Fisher

                                   Gardener

                            Fisher and/or Gardener
     Carcinogens[a]
Arsenic
                                       8
                                     5 (2)
                                   0.05 (0)
                                    0.2 (0)
                                      n/a
Polycyclic Organic Matter as Benzo(a)pyrene TEQ
                                       8

                                    20 (2)
                                     2 (1)
                                   0.02 (0)
                                      n/a
Arsenic + POM
                                       8
                                    20 (3)
                                     2 (1)
                                    0.2 (0)
                                      n/a
     Non-carcinogens
Cadmium Compounds
                                       8
                                     2 (1)
                                    0.2 (0)
                                   0.02 (0)
                                      n/a
Mercury Compounds
                                       8
                                     2 (1)
                                    0.3 (0)
                                  0.0003 (0)
                                      n/a

Notes:
a POM emissions were normalized to BaP for oral toxicity and modeled for environmental fate and transport.

When tiered screening values for any facility indicate a potential health risk to the public, we can conduct a more refined multipathway assessment for a specific facility. A refined assessment replaces some of the assumptions made in the tiered screening with facility-specific information. The maximum Tier 2 screening value for the source categories was 2 for POM, and, as stated above, indicates we are confident that multipathway cancer risks from POM are below 2-in-1 million. Given the health protective nature of the screening assessment and our experience with other multipathway assessments, the risks are likely below 1-in-1 million Therefore, since none of the facilities in this source emitted hazardous air pollutants at a rate that would suggest a potential health risk to the public, a facility-specific multipathway assessment was not warranted. As such, Appendix 11 of this document is intentionally blank.

In evaluating the potential for multipathway effects from emissions of lead, modeled maximum annual lead concentrations were compared to the NAAQS for lead (0.15 ug/m[3]). Lead emissions were reported from 8 facilities. Results of this assessment estimate that the NAAQS for lead would not be exceeded by any facility.

Environmental
We conducted a screening-level evaluation of the potential adverse environmental risks associated with emissions of the following environmental HAP for the asphalt processing and asphalt roofing manufacturing source categories: arsenic, cadmium, hydrochloric acid, lead, mercury, and POMs.
An environmental screening assessment was conducted for PB-HAP. For the Tier 1 environmental screening assessment arsenic, cadmium, mercury, and POM emissions had no Tier 1 exceedances for any ecological benchmark. For lead, we did not estimate any exceedances of the secondary lead NAAQS. For hydrochloric acid, each individual concentration (i.e., each off-site data point in the modeling domain) was below the ecological benchmarks for all facilities.
 Post-control risk characterization
In light of the low cancer and noncancer risk posed to individuals exposed to HAP emitted from these source categories, a post-control risk assessment is not warranted.

 General discussion of uncertainties in the risk assessment
The uncertainties in virtually all of the RTR risk assessments can be divided into three areas: 1) uncertainties in the emission data sets, 2) exposure modeling uncertainties, and 3) uncertainties in the dose-response relationships. Uncertainties in the emission estimates and in the air quality models lead to uncertainty in air concentrations. Uncertainty in exposure modeling can arise due to uncertain activity patterns, the locations of individuals within a census tract, and the microenvironmental concentrations as reflected in the exposure model. Finally, uncertainty in the shape of the relationship between exposure and effects, the URE and the RfC, also contributes to uncertainties in the risk assessment. These three areas of uncertainty are discussed below.
 Emissions inventory uncertainties
Although the development of the RTR emissions data set involves an extensive quality assurance/quality control process, the accuracy of emission values will vary depending on certain factors, for example, the source of the data, the degree to which data are incomplete or missing, the degree to which assumptions made to complete the data sets are accurate, and the extent to which there are errors in these emission estimates. The emission estimates used in the risk assessment generally are annual totals for certain years, and they do not reflect short-term fluctuations during the course of a year or variations from year to year.

For the acute effects screening assessment, therefore, in the absence of available specific estimates or measurements, we use estimates of peak hourly emission rates. These estimates typically are calculated by first estimating the average annual hourly emissions rates by evenly dividing the total annual emission rate from the inventory into the 8,760 hours of the year. An emission adjustment factor that is intended to account for emission fluctuations during normal facility operations is then applied to these average annual hourly emission rates. The adjustment factor can be based on actual fluctuations seen in the available emission data for sources in a category or on engineering judgment; in the absence of such information, a default factor is applied.

To prepare the emissions data set, EPA gathers the best available data on emissions, emission release parameters, and other relevant source category-specific parameters. EPA often begins with its National Emissions Inventory (NEI) database as the starting point for emission rates, emissions release characteristics, and locations of the emission release points for each facility in the source category. The NEI is a composite of emission measurements and estimates produced by state and local regulatory agencies, industry, and EPA. EPA's industry experts then review the data for consistency and completeness and conduct extensive quality assurance/quality control checks. Available information, which may include compliance data, information from project files, permits, and other sources regarding facilities and emission sources, are also incorporated into the data set. This additional information may be incorporated in addition to the NEI data or in place of the NEI data, depending on EPA's evaluation of the quality of the various sources of data. In order to fill data gaps, EPA may conduct a formal information collection request (ICR) under the authority of section 114 of the Clean Air Act to obtain current, complete emissions data and other data from the facility owners and operators associated with the source category under review.

Uncertainty in the emissions data set stems from data gaps, default assumptions, and the emission models used to develop emissions inventory estimates. A variety of methods, such as emission factors, material balances, engineering judgement, air permit information and source testing, are used to develop emission estimates. Other parameters that are part of the emissions data set, including facility location and emission point parameters, may also be a source of uncertainty. Some release point locations use an average facility location instead of the location of each specific unit within the facility. In some instances, default release point parameters may be in the inventory. Where fugitive release parameters are not available, default values are included. Another potential source of emission estimate uncertainty may be low or poor quality data (e.g., out-of-date parameter values). For more information on the uncertainties in the emission estimates for these source categories see Appendix 1 (Emissions Inventory Support Documents) of this document.
 Exposure modeling uncertainties
4.2.1	Inhalation exposure modeling
Although every effort is made to identify all of the relevant facilities 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 the exposure assessment. The ambient air modeling uncertainties are considered relatively small in comparison, since we are using EPA's refined local dispersion model with site-specific parameters and reasonably representative meteorology. If anything, the population exposure estimates are biased high by not accounting for short- or long-term population mobility and by not addressing processes like deposition, plume depletion, and atmospheric degradation. Additionally, estimates of maximum individual risk (MIR) contain uncertainty because they are derived at census block centroid locations rather than actual residences. This uncertainty is known to create potential underestimates and overestimates of the actual MIR values for individual facilities; however, overall, it is not thought to have a significant impact on the estimated MIR for a source category. We also do 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 do we factor in the possibility of population growth during the 70-year chronic exposure period, which could lead to a potential downward bias in both the MIR and population risk estimates. Finally, we do not factor in time an individual spends indoors.

We did not include the effects of human mobility on exposures in the assessment. Specifically, short-term mobility and long-term mobility between census blocks in the modeling domain were not considered. (Short-term mobility is movement from one micro-environment to another over the course of hours or days. Long-term mobility is movement from one residence to another over the course of a lifetime.)  The approach of not considering short or long-term population mobility does not bias the estimate of the theoretical MIR (by definition), nor does it affect the estimate of cancer incidence because 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 high risk levels (e.g., 1-in-10 thousand or 1-in-1 million).

In addition, the assessment predicted the chronic exposures at the centroid of each populated census block as surrogates for the exposure concentrations for all people living in that block. Using the census block centroid to predict chronic exposures tends to over-predict exposures for people in the census block who live farther from the facility and under-predict exposures for people in the census block who live closer to the facility. Thus, using the census block centroid to predict chronic exposures may lead to a potential understatement or overstatement of the true maximum impact, but is an unbiased estimate of average risk and incidence. We reduce this uncertainty by analyzing large census blocks near facilities using aerial imagery and adjusting the location of the block centroid to better represent the population in the block, as well as adding additional receptor locations where the block population is not well represented by a single location.

The assessment evaluates the cancer inhalation risks associated with pollutant exposures over a 70-year period, which is the assumed lifetime of an individual. In reality, both the length of time that modeled emission sources at facilities actually operate (i.e., more or less than 70 years) and the domestic growth or decline of the modeled industry (i.e., the increase or decrease in the number or size of domestic facilities) will influence the future risks posed by a given source or source category. Depending on the characteristics of the industry, these factors will, in most cases, result in an overestimate both in individual risk levels and in the total estimated number of cancer cases. However, in the unlikely scenario where a facility maintains, or even increases, its emissions levels over a period of more than 70 years, residents live beyond 70 years at the same location, and the residents spend more of their days at that location, then the cancer inhalation risks could potentially be underestimated. However, annual cancer incidence estimates from exposures to emissions from these sources would not be affected by the length of time an emissions source operates.

The exposure estimates used in these analyses assume chronic exposures to ambient (outdoor) levels of pollutants. Because most people spend the majority of their time indoors, actual exposures may not be as high, depending on the characteristics of the pollutants modeled. For many of the HAP, indoor levels are roughly equivalent to ambient levels, but for very reactive pollutants or larger particles, indoor levels are typically lower. This factor has the potential to result in an overestimate of 25 to 30 percent of exposures (USEPA, 2001).

A sensitivity analysis, discussed in "Risk and Technology Review (RTR) Risk Assessment Methodologies" (USEPA 2009a), found that the selection of the meteorology data set location could have an impact on the risk estimates. The analysis found that cancer MIR derived using different meteorological stations varied by as much as 63 percent below to 51 percent above the value derived using the nearest meteorological station. Cancer incidence estimated using different meteorological stations varied by as much as 68 percent below to 120 percent above the value estimated using the nearest meteorological station. Similarly, air concentrations estimated using different meteorological stations varied by as much as 49 percent below to 21 percent above the value estimated using the nearest meteorological station. Since this analysis was performed EPA has increased the number of meteorological stations used in our risk assessments; thus, we expect variability to be reduced.

For the acute screening assessment, the results are intentionally biased high, and thus health-protective, by assuming the co-occurrence of independent factors, such as hourly emission rates, meteorology and human activity patterns. Furthermore, in cases where multiple acute dose-response values for a pollutant are considered scientifically acceptable, we choose the most conservative of these dose-response values, erring on the side of overestimating potential health risks from acute exposures. In cases where these results indicate the potential for exceeding acute HQs, we refine our assessment by developing a better understanding of the geography of the facility relative to potential exposure locations.

4.2.2	Multipathway exposure modeling
In modeling the fate and transport of pollutants through the environment and the non-inhalation exposure (i.e., ingestion) to these pollutants, TRIM.FaTE uses simplified representations of many complex real-world processes. This simplified representation introduces uncertainty. Uncertainties arise from model assumptions and structure, as reflected in the algorithms that describe the environmental movement of pollutants, and in the input values for numerous environmental parameters.

Uncertainty in the algorithms is inherent to any model attempting to represent complex processes in the real world. How persistent, bioaccumulative chemicals such as mercury, cadmium, arsenic, PAHs, and dioxins behave in the environment is highly complex, and many natural processes are represented in a simplified manner by TRIM.FaTE, including, for example:

 gaseous and particulate deposition from air;
 biogeochemical cycling in the aquatic environment, particularly mercury transformations through methylation and demethylation at the sediment-surface interface;
 mixing processes in air, water, and sediment;
 suspended and benthic sediment dynamics in lakes; and
 biotic processes such as growth, reproduction, and predation.

Even though some processes, such as diffusion, are known to follow second-order dynamics, the TRIM.FaTE model represents all fate and transport processes in terms of first-order differential equations. TRIM.FaTE also does not explicitly deal with lateral or vertical dispersion in the air compartments. Some algorithms, such as those addressing methylation and sediment transport, for example, do not consider all of the factors known to affect the process. Biotic processes including chemical absorption, chemical elimination, growth, reproduction, predation, and death have been represented relatively simplistically in the model. Although the model's algorithms have been validated and are based on professional judgment, some level of uncertainty results from such simplifications.

The input values for parameters are also associated with uncertainty. Algorithms that describe the environmental movement of pollutants depend on numerous environmental parameters for which the values might be naturally variable and for which available data are often limited. Examples of parameters for which input values are variable and uncertain include aquatic food web structure (e.g., diet of each fish species), biokinetic parameters that influence bioaccumulation (e.g., assimilation efficiencies and elimination rates), topographic characteristics (e.g., lake depth, runoff rates, and erosion rates), meteorological parameters (e.g., evaporation and precipitation rates), chemical transformation rates (e.g., methylation and demethylation rates, in the case of mercury), and human exposure parameters (especially fish consumption rates).

For TRIM.FaTE modeling, we use central tendency values and combinations of values that would lead to estimates of reasonable maximum exposures to bound risk estimates. We have conducted analyses of the sensitivity of risk estimates to parameter input values. For those parameters to which the model is particularly sensitive, we have continued to collect additional data to better quantify the variability and distribution of input values.
A more comprehensive explanation of the uncertainties related to fate, transport, and exposure modeling using TRIM.FaTE is provided in Appendix 6 (Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR) of this report for the tiered assessments and Appendix 11 (Site-Specific Human Health Multipathway Residual Risk Assessment Report) of this report for a site-specific assessment if one was conducted.

4.2.3	Environmental risk screening assessment

For each source category, we generally rely on site-specific levels of environmental HAP emissions to perform an environmental screening assessment. The environmental screening assessment is based on the outputs from models that estimate environmental HAP concentrations. The same models, specifically the TRIM.FaTE multipathway model and the AERMOD air dispersion model, are used to estimate environmental HAP concentrations for both the human multipathway screening analysis and for the environmental screening analysis. Therefore, both screening assessments have similar modeling uncertainties. Two important types of uncertainty associated with the use of these models in RTR environmental screening assessments (and inherent to any assessment that relies on environmental modeling) are model uncertainty and input uncertainty.

Model uncertainty concerns whether the selected models are appropriate for the assessment being conducted and whether they adequately represent the movement and accumulation of environmental HAP emissions in the environment. For example, does the model adequately describe the movement of the pollutant through the soil?  This type of uncertainty is difficult to quantify. However, based on feedback received from previous EPA SAB reviews and other reviews, we are confident that the models used in the screening assessments are appropriate and state-of-the-art for the environmental risk assessments conducted in support of our RTR analyses.

Input uncertainty is concerned with how accurately the models have been configured and parameterized for the assessment at hand. For Tier 1 of the environmental screening assessment for PB-HAP, we configured the models to avoid underestimating exposure and risk to reduce the likelihood that the results indicate the risks are lower than they actually are. This was accomplished by selecting upper-end values from nationally-representative datasets for the more influential parameters in the environmental model, including selection and spatial configuration of the area of interest, the location and size of any bodies of water, meteorology, surface water and soil characteristics, and structure of the aquatic food web. In Tier 1, we use the maximum facility-specific emissions for the PB-HAP (other than lead compounds, which were evaluated by comparison to the Secondary Lead NAAQS) that are included in the environmental screening assessment and each of the media when comparing to ecological benchmarks. This is consistent with the conservative design of the Tier 1 screening assessment. In Tier 2 of the environmental screening assessment for PB-HAP, we refine the model inputs to account for meteorological patterns in the vicinity of the facility versus using upper-end national values, and we identify the locations of water bodies near the facility location. By refining the screening approach in Tier 2 to account for local geographical and meteorological data, we decrease the likelihood that concentrations in environmental media are overestimated, thereby increasing the usefulness of the screening assessment. To better represent widespread impacts, the modeled soil concentrations are averaged in Tier 2 to obtain one average soil concentration value for each facility and for each PB-HAP. For PB-HAP concentrations in water, sediment, and fish tissue, the highest value for each facility for each pollutant is used.

For the environmental screening assessment for acid gases, we employ a single-tiered approach. We use the modeled air concentrations and compare those with ecological benchmarks.

For both Tiers 1 and 2 of the environmental screening assessment, our approach to addressing model input uncertainty is generally cautious. We choose model inputs from the upper end of the range of possible values for the influential parameters used in the models, and we assume that the exposed individual exhibits ingestion behavior that would lead to a high total exposure. This approach reduces the likelihood of not identifying potential risks for adverse environmental impacts.

 Uncertainties in the dose-response relationships
 In the sections that follow, separate discussions are provided on uncertainty associated with cancer potency factors and for noncancer reference values. Cancer potency values are derived for chronic (lifetime) exposures. Noncancer dose-response values are generally derived for chronic exposures (up to a lifetime) but may also be derived for acute (less than 24 hours), short-term (from 24 hours up to 30 days), and subchronic (30 days up to 10 percent of lifetime) exposure durations, all of which are derived based on an assumption of continuous exposure throughout the duration specified. For the purposes of assessing all potential health risks associated with the emissions included in an assessment, we rely on both chronic (cancer and noncancer) and acute (noncancer) dose-response values, which are described in more detail below.

Although every effort is made to identify peer-reviewed dose-response values for all HAP emitted by the source category included in an assessment, some HAP have no peer-reviewed values. Since exposures to these pollutants cannot be included in a quantitative risk estimate, an understatement of risk for these pollutants at estimated exposure levels is possible. To help alleviate this potential underestimate, where we conclude similarity with a HAP for which a dose-response assessment value is available, we use that value as a surrogate for the assessment of the HAP for which no value is available. To the extent use of surrogates indicates appreciable risk, we may identify a need to increase priority for a new IRIS assessment of that substance. We additionally note that, generally speaking, HAP of greatest concern due to environmental exposures and hazards are those for which dose-response assessments have been performed, reducing the likelihood of understating risk. Further, HAP not included in the quantitative assessment are assessed qualitatively and considered in the risk characterization that informs the risk management decisions, including with regard to consideration of HAP reductions achieved by various control options.

Additionally, chronic dose-response values for certain compounds included in the assessment may be under EPA IRIS review. In those cases, revised assessments may determine in the future that these pollutants are more or less potent than currently thought.

For a group of compounds that are unspeciated (e.g., glycol ethers), we conservatively use the most protective reference value of an individual compound in that group to estimate risk. Similarly, for an individual compound in a group (e.g., ethylene glycol diethyl ether) that does not have a specified reference value, we apply the most protective reference value from the other compounds in the group to estimate risk.

Cancer assessment
The discussion of dose-response uncertainties in the estimation of cancer risk below focuses on the uncertainties associated with the specific 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 source of peer-reviewed cancer potency factors used where EPA-developed values are not yet available. To place this discussion in context, we provide a quote from the EPA's Guidelines for Carcinogen Risk Assessment (herein referred to as Cancer Guidelines). (USEPA, 2005d)  "The primary goal of EPA actions is protection of human health; accordingly, as an Agency policy, risk assessment procedures, including default options that are used in the absence of scientific data to the contrary, should be health protective."  The approach adopted in this document is consistent with this approach as described in the Cancer Guidelines.

For cancer endpoints EPA usually derives an oral slope factor for ingestion 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 indicators of potential human health risk when other human cancer risk data are unavailable.

Extrapolation of study data to estimate potential 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 describe the Agency's recommendations for methodologies for cancer risk 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). In some circumstances, the true risk could be as low as zero; however, in other circumstances the risk could also be greater.   When developing an upper bound estimate of risk and to provide risk values that do not underestimate risk, EPA generally relies on conservative default approaches.   EPA also uses the upper bound (rather than lower bound or central tendency) 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 which may be considered quantitatively, and others which generally are expressed qualitatively. Uncertainties may vary substantially among cancer risk assessments associated with exposures to different pollutants, since the assessments employ different databases with different strengths and limitations and the procedures employed may differ in how well they represent actual biological processes for the assessed substance. Some of the major sources of uncertainty and variability in deriving cancer risk values are described more fully below.

(1) The qualitative similarities or differences between tumor responses observed in experimental animal bioassays and those which would occur in humans are a source of uncertainty in cancer risk assessments. In general, EPA does not assume that tumor sites observed in an experimental animal bioassay are necessarily predictive of the sites at which tumors would occur in humans.   However, unless scientific support is available to show otherwise, EPA assumes that tumors in animals are relevant in humans, regardless of target organ concordance. For a specific pollutant, qualitative differences in species responses can lead to either under-estimation or over-estimation of human cancer risks.

(2) Uncertainties regarding the most appropriate dose metric for an assessment can also lead to differences in risk predictions. For example, the measure of dose is commonly expressed in units of mg/kg/d ingested or the inhaled concentration of the pollutant. However, data may support development of a pharmacokinetic model for the absorption, distribution, metabolism and excretion of an agent, which may result 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 choice of a dose metric is uncertain or when dose metric estimates are themselves uncertain (e.g., as can occur when alternative pharmacokinetic models are available for a compound). Uncertainty in dose estimates may lead to either over or underestimation of risk.

(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to humans, EPA uses scaling methodologies (relating expected response to differences in physical size of the species), which introduce another source of uncertainty. These methodologies are based on both biological data on differences in rates of process according to species size and empirical comparisons of toxicity between experimental animals and humans. For a particular pollutant, the quantitative 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 calculation. Statistical uncertainty in developing a POD using a benchmark dose (BMD) approach is generally addressed though use of the 95 percent lower confidence limit on the dose at which the specified 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 further discussion below).

(5) Extrapolation from high to low doses is an important 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 epidemiological studies at higher dose) depending on the available data and understanding 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, biologically-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 consideration in determining how risks 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. Linear extrapolation can be supported by both pollutant-specific data and broader scientific considerations. For example, EPA's Cancer Guidelines generally consider a linear dose-response to be appropriate for pollutants that interact with DNA and induce mutations. Pollutants whose effects are 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 estimated by the straight line approach.

EPA most frequently utilizes a linear low-dose extrapolation approach as a baseline science-policy 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 process, provide an upper bound estimate of risk and generally provide a health protective approach. Note that another source of uncertainty is the characterization of low-dose nonlinear, non-threshold relationships. The National Academy of Sciences (NAS, 1994) has encouraged the exploration of sigmoidal type functions (e.g., log-probit models) in representing dose-response relationships due to the variability in response within human populations. Another National Research Council report (NRC, 2006) 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 individual tolerances to vary extensively in humans depending on genetics, coincident 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 response in human populations (as opposed to responses in bioassays with genetically more uniform rodents). Note also that low dose linearity in risk can arise for reasons separate from population variability: due 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 equivalent to using an upper confidence limit on the slope of the straight line extrapolation. The impact of the choice of the BMDL on bottom line risk estimates can be quantified by comparing risk 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). 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 straight 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  --  resulting 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 from 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 are designed not to underestimate risk in the face of uncertainty and variability.

Chronic noncancer assessment
Chronic noncancer reference values represent chronic exposure levels that are intended to be health-protective. That is, EPA and other organizations, such as the Agency for Toxic substances and disease Registry (ATSDR), which develop noncancer dose-response values use an approach that is intended not to underestimate risk in the face of uncertainty and variability. When there are gaps in the available information, 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 (e.g., factors of 10 or 3) used in the absence of compound-specific data; where data are available, uncertainty factors may also be developed using compound-specific information. When data are limited, more assumptions are needed and more default factors are used. Thus, there may be a greater tendency to overestimate risk -- in the sense that further study might support development of reference values that are higher (i.e., less potent) because fewer default assumptions are needed. However, for some pollutants it is possible that risks may be underestimated.

For noncancer endpoints related to chronic exposures, EPA derives a reference dose (RfD) for exposures via ingestion, and a reference concentration (RfC) for inhalation exposures. As stated in the IRIS Glossary, these values provide an estimate (with uncertainty spanning perhaps an order of magnitude) of daily oral exposure (RfD) or of a continuous inhalation exposure (RfC) to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. To derive values that are intended to be "without appreciable risk," EPA's methodology relies upon an uncertainty factor (UF) approach (USEPA, 1993b; USEPA, 1994b) which includes consideration of both uncertainty and variability.

EPA begins by evaluating all of the available peer-reviewed literature to determine noncancer endpoints of concern, evaluating the quality, strengths and limitations of the available studies. EPA typically chooses the relevant endpoint that occurs at the lowest dose, often using statistical modeling of the available data, and then determines the appropriate POD for derivation of the reference value. A POD is determined by (in order of preference): (1) a statistical estimation using the BMD approach; (2) use of the dose or concentration at which the toxic response was not significantly elevated (no observed adverse effect level --  NOAEL); or (3) use of the lowest observed adverse effect level (LOAEL).

A series of downward adjustments using default UFs is then applied to the POD to estimate the reference value (USEPA, 2002b). While collectively termed "UFs", these factors account for a number of different quantitative considerations when utilizing 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-individual variability); (2) uncertainty in extrapolating from experimental animal data to humans (i.e., interspecies differences); (3) uncertainty in extrapolating from data obtained in a study with less-than-lifetime exposure (i.e., extrapolating 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 problems with applicability of available studies. When scientifically sound, peer-reviewed assessment-specific data are not available, default adjustment values are selected for the individual UFs. For each type of uncertainty (when relevant to the assessment), EPA typically applies 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.

	1) Heterogeneity among humans is a key source of variability as well as uncertainty. Uncertainty related to human variation is considered 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 variation may be larger or smaller; however, data to examine the potential magnitude of human variability are often unavailable. In some situations, a smaller UF of 3 may be applied to reflect a known lack of significant variability among humans.

	2) Extrapolation from results of studies in experimental animals to humans is a necessary step for the majority of chemical risk assessments. When interpreting animal data, the concentration at the POD (e.g. NOAEL, BMDL) in an animal model (e.g. rodents) is extrapolated to estimate the human response. While there is long-standing scientific support for the use of animal studies as indicators of potential toxicity to humans, there are uncertainties in such extrapolations. 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 average human. Typically, compound specific data to evaluate relative sensitivity in humans versus rodents are lacking, 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 consistent 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 or toxicodynamic differences between animals and 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 associated 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 available 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 chronic exposures where only data from shorter durations are available (e.g., 90-day subchronic studies in rodents) 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 the 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 preferred approach to arrive at a POD is to use BMD analysis; however, this approach requires adequate 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 which 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 account for the potential for deriving an underprotective RfD/RfC due to a data gap preventing complete characterization of the chemical's toxicity. In the absence of studies for a known or suspected 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 developed for chronic durations. For acute reference values, though, individual UF values may be less than 10. UFs are applied based on chemical- or health effect-specific information or based on the purpose of the reference value. The UFs applied in acute reference value derivation include:  1) heterogeneity among humans; 2) uncertainty in extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 4) uncertainty in accounting for an incomplete database on toxic effects of potential concern. Additional adjustments are often applied to account for uncertainty in extrapolation from observations at one exposure duration (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 dose-response values are developed for the same purpose and care must be taken when interpreting the results of an acute assessment of human health effects relative to the reference value or values being exceeded. Where relevant to the estimated exposures, the lack of dose-response values at different levels of severity should be factored into the risk characterization as potential uncertainties.

Environmental Risk Screening Assessment
Uncertainty also exists in the ecological benchmarks for the environmental risk screening assessment. We established a hierarchy of preferred benchmark sources to allow selection of benchmarks for each environmental HAP at each ecological assessment endpoint. In general, EPA benchmarks used at a programmatic level (e.g., Office of Water, Superfund Program) were used if available. If not, we used EPA benchmarks used in regional programs (e.g., Superfund Program). If benchmarks were not available at a programmatic or regional level, we used benchmarks developed by other agencies (e.g., NOAA) or by state agencies.

In all cases (except for lead compounds, which were evaluated through a comparison to the NAAQS), we searched for benchmarks at the following three effect levels, as described in Section 2.6 of this report and in Appendix 9 (Environmental Risk Screening Assessment) of this report:  a no-effect level (i.e., NOAEL), threshold-effect level (i.e., LOAEL), and probable-effect level (i.e., PEL).

For some ecological assessment endpoint/environmental HAP combinations, we could identify benchmarks for all three effect levels, but for most we could not. In one case, where different agencies derived significantly different numbers to represent a threshold for effect, we included both. In several cases, only a single benchmark was available. In cases where multiple effect levels were available for a particular PB-HAP and assessment endpoint, we used all of the available effect levels to help us determine whether risk exists if risks could be considered significant and widespread.

 References

Allen, et al., 2004. Variable Industrial VOC Emissions and their impact on ozone formation in the Houston Galveston Area. Texas Environmental Research Consortium. https://www.researchgate.net/publication/237593060_Variable_Industrial_VOC_Emissions and_their_Impact_on_Ozone_Formation_in_the_Houston_Galveston_Area

ATSDR, 2012. Agency for Toxic Substances & Disease Registry Toxicological Profile for Manganese. http://www.atsdr.cdc.gov/toxprofiles/tp.asp?id=102&tid=23

Burger, J. 2002. Daily consumption of wild fish and game: Exposures of high end recreationalists. Environmental Health Research. 12(4):343 - 354.

Grimsrud TK and Andersen A., 2010. Evidence of carcinogenicity in humans of water-soluble nickel salts. J Occup Med Toxicol 2010, 5:1-7. Available online at http://www.occup-med.com/content/5/1/7

Herr, D.W., Graff, J.E., Moser, V.C., Crofton, K.M., Little, P.B., Morgan, D.L., and Sills, R.C., 2007. Inhalational exposure to carbonyl sulfide produced altered brainstem auditory and somatosensory-evoked potentials in Fischer 344N rats. Toxicol. Sci. 95(1):118-135, 2007.

IARC, 1990. International Agency for Research on Cancer, 1990. IARC monographs on the evaluation of carcinogenic risks to humans. Chromium, nickel, and welding. Vol. 49. Lyons, France:  International Agency for Research on Cancer, World Health Organization Vol. 49:256.

Morgan, D.L., Little, P.B., Herr, D.W., Moser, V.C., Collins, B., Herbert, R., Johnson, G.A., Maronpot, R.R., Harry, G.J., and Sills, R.C., 2004. Neurotoxicity of carbonyl sulfide in F344 rats following inhalation exposure for up to 12 weeks. Toxicol. Appl. Pharmacol. 200(2):131- 145, 2004.

National Academy of Sciences, 1994. National Research Council. Science and Judgement in Risk Assessment. Washington, DC: National Academy Press.

NRC, 2006. National Research Council. Assessing the Human Health Risks of Trichloroethylene. National Academies Press, Washington DC.

NTP, 2016. National Toxicology Program. 2016. Report on Carcinogens, Fourteenth Edition.; Research Triangle Park, NC: U.S. Department of Health and Human Services, Public Health Service. https://ntp.niehs.nih.gov/pubhealth/roc/index-1.html

OEHHA, 2015. California Office of Environmental Health Hazard Assessment. All Acute Reference Exposure Levels developed by OEHHA as of June 2014. http://oehha.ca.gov/air/allrels.html

OMB, 2007. 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 Deputy Director for Science, Office of Science and Technology Policy.
https://georgewbush-whitehouse.archives.gov/omb/memoranda/fy2007/m07-24.pdf

R.P. Subramaniam, P. White and V.J. Cogliano. 2006. Comparison of cancer slope factors using different statistical approaches, Risk Anal. Vol 26, p. 825-830.

Roels HA, Ghyselen P, Buchet JP, et al. 1992. Assessment of the permissible exposure level to manganese in workers exposed to manganese dioxide dust. Br J Ind Med 49:25-34.

Sills, R.C., Morgan, D.L., Herr, D.W., Little, P.B., George, N.M., Ton, T.V., Love, N.E., Maronpot, R.R., and Johnson, G.A., 2004. Contribution of magnetic resonance microscopy in the 12-week neurotoxicity evaluation of carbonyl sulfide in Fischer 344 rats. Toxicol. Pathol. 32:501-510, 2004.

USEPA, 1986. Guidelines for the Health Risk Assessment of Chemical Mixtures. EPA-630-R-98-002. https://www.epa.gov/risk/guidelines-health-risk-assessment-chemical-mixtures

USEPA, 1993a. Integrated Risk Information system Review of Manganese. https://cfpub.epa.gov/ncea/iris2/chemicalLanding.cfm?substance_nmbr=373

USEPA, 1993b. Reference Dose (RfC):  Description and Use in Health Risk Assessments.           https://www.epa.gov/iris/reference-dose-rfd-description-and-use-health-risk-assessments

USEPA, 1994a. US Environmental Protection Agency. Methods for Derivation of Inhalation Reference Concentrations and Application of Inhalation Dosimetry. EPA/600/8-90/066F. Office of Research and Development. Washington, DC: U.S.EPA.

USEPA, 1994b. Methods for Derivation of Inhalation Reference Concentrations and Application of Inhalation Dosimetry. https://www.epa.gov/risk/methods-derivation-inhalation-reference-concentrations-and-application-inhalation-dosimetry

USEPA, 1999. Residual Risk Report to Congress. 453R-99-001. https://www3.epa.gov/airtoxics/rrisk/risk_rep.pdf

USEPA, 2000a. Risk Characterization Handbook. EPA 100-B-00-002. https://www.epa.gov/sites/production/files/2015-10/documents/osp_risk_characterization_handbook_2000.pdf

USEPA, 2000b. Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures. EPA-630/R-00-002. https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=20533

USEPA, 2001. National-Scale Air Toxics Assessment for 1996. EPA 453/R - 01 - 003. January 2001. page 85.

USEPA, 2002a. EPA's Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Information Disseminated by the Environmental Protection Agency. EPA Office of Environmental Information. EPA/260R-02-008. https://www.epa.gov/quality/guidelines-ensuring-and-maximizing-quality-objectivity-utility-and-integrity-information

USEPA, 2002b. A Review of the Reference Dose and Reference Concentration Processes. https://www.epa.gov/osa/review-reference-dose-and-reference-concentration-processes

USEPA, 2005a. Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 40 CFR Part 51. https://www3.epa.gov/scram001/guidance/guide/appw_05.pdf

USEPA, 2005b. Supplemental guidance for assessing early-life exposure to carcinogens. EPA/630/R-03003F. https://www3.epa.gov/ttn/atw/childrens_supplement_final.pdf

USEPA, 2005c. Science Policy Council Cancer Guidelines Implementation Workgroup Communication I: Memo from W.H. Farland dated 4 October 2005 to Science Policy Council. https://www.epa.gov/sites/production/files/2015-01/documents/cgiwgcommuniation_i.pdf

USEPA, 2005d. Guidelines for Carcinogen Risk Assessment. U.S. Environmental Protection Agency, Washington, DC, EPA/630/P-03/001F. https://www.epa.gov/risk/guidelines-carcinogen-risk-assessment

USEPA, 2006. Performing risk assessments that include carcinogens described in the Supplemental Guidance as having a mutagenic mode of action. Science Policy Council Cancer Guidelines Implementation Workgroup Communication II: Memo from W.H. Farland dated 14 June 2006. https://www.epa.gov/sites/production/files/2015-01/documents/cgiwg-communication_ii.pdf

USEPA, 2009a. Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA's Science Advisory Board with Case Studies  -  MACT I Petroleum Refining Sources and Portland Cement Manufacturing. EPA-452/R-09-006. https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryID=238928

USEPA, 2009b. Graphical Arrays of Chemical-Specific Health Effect Reference Values for Inhalation Exposures [Final Report]. EPA/600/R-09/061, 2009. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003

USEPA, 2010a. SAB's Response to EPA's RTR Risk Assessment Methodologies. https://yosemite.epa.gov/sab/sabproduct.nsf/0/b031ddf79cffded38525734f00649caf!OpenDocument&TableRow=2.3#2

US EPA, 2010b. Memorandum from Dave Guinnup to Docket EPA-HQ-OAR-2010-0600, entitled, "EPA's Actions in Response to Key Recommendations of the SAB Review of RTR Risk Assessment Methodologies". https://yosemite.epa.gov/sab/sabproduct.nsf/3BE2C36A4ADDC85A85257B48006C88D7/$File/EPA+resp+to+SAB+on+RTR+memo.pdf

USEPA, 2011. Exposure Factors Handbook: 2011 Edition (Final). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-09/052F. https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252

USEPA, 2014a. Table 1. Prioritized Chronic Dose-Response Values (5/9/14). Office of Air Quality Planning and Standards. https://www.epa.gov/fera/dose-response-assessment-assessing-health-risks-associated-exposure-hazardous-air-pollutants

USEPA, 2014b. Table 2. Acute Dose-Response Values for Screening Risk Assessments (9/18/2014). Office of Air Quality Planning and Standards. https://www.epa.gov/fera/dose-response-assessment-assessing-health-risks-associated-exposure-hazardous-air-pollutants

USEPA, 2016a User's Guide for the AMS/EPA Regulatory Model (AERMOD). EPA-454/B-16-011, U.S. Environmental Protection Agency, Research Triangle Park, NC.
https://www3.epa.gov/ttn/scram/models/aermod/aermod_userguide.pdf

USEPA, 2016b. AERMOD Implementation Guide. EPA-454/B-16-013, U.S. Environmental Protection Agency, Research Triangle Park, NC.
https://www3.epa.gov/ttn/scram/models/aermod/aermod_implementation_guide.pdf

WHO, 1991. World Health Organization and the European Union's Scientific Committee on Health and Environmental Risks (SCHER, 2006).


[Federal Register Volume 78, Number 190 (Tuesday, October 1, 2013)]
[Notices]
[Pages 60334-60335]
[FR Doc No: 2013-23872]

=======================================================================
-----------------------------------------------------------------------

SECURITIES AND EXCHANGE COMMISSION

Proposed Collection; Comment Request

Upon Written Request, Copies Available From: Securities and Exchange
Commission, Office of Investor Education and Advocacy, Washington, DC
20549-0213.

Extension: Rules 201 and 200(g) of Regulation SHO

SEC File No. 270-606, OMB Control No. 3235-0670

    Notice is hereby given that pursuant to the Paperwork Reduction Act
of 1995 (44 U.S.C. 3501 et seq.) (``PRA''), the Securities and Exchange
Commission (``Commission'') is soliciting comments on the existing
collection of information provided for in Rule 201 (17 CFR 242.201) and
Rule 200(g) (17 CFR 242.200(g)) under the Securities Exchange Act of
1934 (15 U.S.C. 78a et seq.). The Commission plans to submit this
existing collection of information to the Office of Management and
Budget (``OMB'') for extension and approval.
    Rule 201 is a short sale-related circuit breaker rule that, if
triggered, imposes a restriction on the prices at which securities may
be sold short. Rule 200(g) provides that a broker-dealer may mark
certain qualifying sell orders ``short exempt.'' The information
collected under Rule 201's written policies and procedure requirement
applicable to trading centers, the written policies and procedures
requirement of the broker-dealer provision of Rule 201(c), the written
policies and procedures requirement of the riskless principal provision
of Rule 201(d)(6), and the ``short exempt'' marking requirement of Rule
200(g) enable the Commission and SROs to examine and monitor for
compliance with the requirements of Rule 201 and Rule 200(g).
    In addition, the information collected under Rule 201's written
policies and procedure requirement applicable to trading centers help
ensure that trading centers do not execute or display any impermissibly
priced short sale orders, unless an order is marked ``short exempt,''
in accordance with the Rule's requirements. Similarly, the information
collected under the written policies and procedures requirement of the
broker-dealer provision of Rule 201(c) and the riskless principal
provision of Rule 201(d)(6) help to ensure that broker-dealers comply
with the requirements of these provisions. The information collected
pursuant to the new ``short exempt'' marking requirement of Rule 200(g)
also provide an indication to a

trading center when it must execute or display a short sale order
without regard to whether the short sale order is at a price that is
less than or equal to the current national best bid.
    It is estimated that SRO and non-SRO respondents registered with
the Commission and subject to the collection of information
requirements of Rules 201 and 200(g) incur an aggregate annual burden
of 2,029,276 hours to comply with the Rules and an aggregate annual
external cost of $65,928,700.
    Written comments are invited on: (a) Whether the proposed
collection of information is necessary for the proper performance of
the functions of the Commission, including whether the information
shall have practical utility; (b) the accuracy of the Commission's
estimates of the burden of the proposed collection of information; (c)
ways to enhance the quality, utility, and clarity of the information
collected; and (d) ways to minimize the burden of the collection of
information on respondents, including through the use of automated
collection techniques or other forms of information technology.
Consideration will be given to comments and suggestions submitted in
writing within 60 days of this publication.
    An agency may not conduct or sponsor, and a person is not required
to respond to, a collection of information unless it displays a
currently valid OMB control number.
    Please direct your written comments to: Thomas Bayer, Director/
Chief Information Officer, Securities and Exchange Commission, c/o Remi
Pavlik-Simon, 100 F Street NE., Washington, DC 20549, or send an email
to<EMAIL_ADDRESS>
    Dated: September 26, 2013.
Jill M. Peterson,
Assistant Secretary.
[FR Doc. 2013-23872 Filed 9-30-13; 8:45 am]
BILLING CODE 8011-01-P


[Federal Register Volume 77, Number 233 (Tuesday, December 4, 2012)]
[Notices]
[Page 71792]
[FR Doc No: 2012-29252]

=======================================================================
-----------------------------------------------------------------------

ENVIRONMENTAL PROTECTION AGENCY

[FRL-9757-6]

Cross-Media Electronic Reporting: Authorized Program Revision
Approval, State of Georgia

AGENCY: Environmental Protection Agency (EPA).

ACTION: Notice.

-----------------------------------------------------------------------

SUMMARY: This notice announces EPA's approval of the State of Georgia's
request to revise certain of its EPA-authorized programs to allow
electronic reporting.

DATES: EPA's approval is effective December 4, 2012.

FOR FURTHER INFORMATION CONTACT: Evi Huffer, U.S. Environmental
Protection Agency, Office of Environmental Information, Mail Stop
2823T, 1200 Pennsylvania Avenue NW., Washington, DC 20460, (202) 566-
1697<EMAIL_ADDRESS>U.S. Environmental Protection Agency, Office
of Environmental Information, Mail Stop 2823T, 1200 Pennsylvania Avenue
NW., Washington, DC 20460, or Karen Seeh, U.S. Environmental Protection
Agency, Office of Environmental Information, Mail Stop 2823T, 1200
Pennsylvania Avenue NW., Washington, DC 20460,<PHONE_NUMBER>,
<EMAIL_ADDRESS>
SUPPLEMENTARY INFORMATION: On October 13, 2005, the final Cross-Media
Electronic Reporting Rule (CROMERR) was published in the Federal
Register (70 FR 59848) and codified as part 3 of title 40 of the CFR.
CROMERR establishes electronic reporting as an acceptable regulatory
alternative to paper reporting and establishes requirements to assure
that electronic documents are as legally dependable as their paper
counterparts. Under subpart D of CROMERR, state, tribe or local
government agencies that receive, or wish to begin receiving,
electronic reports under their EPA-authorized programs must apply to
EPA for a revision or modification of those programs and obtain EPA
approval. Subpart D also provides standards for such approvals based on
consideration of the electronic document receiving systems that the
state, tribe, or local government will use to implement the electronic
reporting. Additionally, in Sec.  3.1000(b) through (e) of 40 CFR part
3, subpart D provides special procedures for program revisions and
modifications to allow electronic reporting, to be used at the option
of the state, tribe or local government in place of procedures
available under existing program-specific authorization regulations. An
application submitted under the subpart D procedures must show that the
state, tribe or local government has sufficient legal authority to
implement the electronic reporting components of the programs covered
by the application and will use electronic document receiving systems
that meet the applicable subpart D requirements.
    On April 16, 2012, the Georgia Department of Natural Resources (GA
DNR) submitted an application titled ``Network Discharge Monitoring
Report (NetDMR)'' electronic document receiving system for revision of
its EPA-authorized programs under title 40 CFR. EPA reviewed GA DNR's
request to revise its EPA-authorized programs and, based on this
review, EPA determined that the application met the standards for
approval of authorized program revisions set out in 40 CFR part 3,
subpart D. In accordance with 40 CFR 3.1000(d), this notice of EPA's
decision to approve Georgia's request for revision to its 40 CFR part
123- National Pollutant Discharge Elimination System (NPDES) State
Program Requirements and part 403--General Pretreatment Regulations for
Existing and New Sources of Pollution EPA-authorized programs for
electronic reporting of information submitted under 40 CFR parts 122
and 403 is being published in the Federal Register.
    GA DNR was notified of EPA's determination to approve its
application with respect to the authorized programs listed above.

    Dated: November 19, 2012.
Andrew Battin,
Director,
    Office of Information Collection.
[FR Doc. 2012-29252 Filed 12-3-12; 8:45 am]
BILLING CODE 6560-50-P