[Federal Register Volume 84, Number 123 (Wednesday, June 26, 2019)]
[Proposed Rules]
[Pages 30524-30569]
[FR Doc No: 2019-12773]

Vol. 84

Wednesday,

No. 123

June 26, 2019

Part IV

Environmental Protection Agency

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

40 CFR Parts 141 and 142

National Primary Drinking Water Regulations: Perchlorate; Proposed Rule

Federal Register / Vol. 84, No. 123 / Wednesday, June 26, 2019 /
Proposed Rules

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

ENVIRONMENTAL PROTECTION AGENCY

40 CFR Parts 141 and 142

[EPA-HQ-OW-2018-0780; FRL-9994-68-OW]
RIN 2040-AF28

National Primary Drinking Water Regulations: Perchlorate

AGENCY: Environmental Protection Agency (EPA).

ACTION: Proposed rule, request for public comment.

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

SUMMARY: The Environmental Protection Agency (EPA) is proposing a
drinking water regulation for perchlorate and a health-based Maximum
Contaminant Level Goal (MCLG) in accordance with the Safe Drinking
Water Act (SDWA). The EPA is proposing to set both the enforceable
Maximum Contaminant Level (MCL) for the perchlorate regulation and the
perchlorate MCLG at 0.056 mg/L (56 [micro]g/L). The EPA is proposing
requirements for water systems to conduct monitoring and reporting for
perchlorate and to provide information about perchlorate to their
consumers through public notification and consumer confidence reports.
This proposal includes requirements for primacy agencies that implement
the public water system supervision program under the SDWA. This
proposal also includes a list of treatment technologies that would
enable water systems to comply with the MCL, including affordable
compliance technologies for small systems serving 10,000 persons or
less.

DATES: Comments must be received on or before August 26, 2019. Under
the Paperwork Reduction Act (PRA), comments on the information
collection provisions are best assured of consideration if the Office
of Management and Budget (OMB) receives a copy of your comments on or
before July 26, 2019.

ADDRESSES: Submit your comments, identified by Docket ID No. EPA-HQ-OW-
2018-0780, at https://www.regulations.gov. Follow the online
instructions for submitting comments. Once submitted, comments cannot
be edited or removed from Regulations.gov. The EPA may publish any
comment received to its public docket. Do not submit electronically any
information you consider to be Confidential Business Information (CBI)
or other information whose disclosure is restricted by statute.
Multimedia submissions (audio, video, etc.) must be accompanied by a
written comment. The written comment is considered the official comment
and should include discussion of all points you wish to make. The EPA
will generally not consider comments or comment contents located
outside of the primary submission (i.e., on the web, cloud, or other
file sharing system). For additional submission methods, the full EPA
public comment policy, information about CBI or multimedia submissions,
and general guidance on making effective comments, please visit http://www2.epa.gov/dockets/commenting-epa-dockets.

FOR FURTHER INFORMATION CONTACT: Samuel Hernandez, Office of Ground
Water and Drinking Water, Standards and Risk Management Division (Mail
Code 4607M), Environmental Protection Agency, 1200 Pennsylvania Avenue
NW, Washington, DC 20460; telephone number:<PHONE_NUMBER>; email
address<EMAIL_ADDRESS>
SUPPLEMENTARY INFORMATION: In addition to the proposed regulation, the
EPA is requesting comment on three alternatives: (1) Whether the MCL
and MCLG for perchlorate should be set at 0.018 mg/L (18 [micro]g/L),
(2) whether the MCL and MCLG for perchlorate should be set at 0.090 mg/
L (90 [micro]g/L), or (3) whether instead of issuing a national primary
drinking water regulation, the EPA should withdraw the Agency's
February 11, 2011, determination to regulate perchlorate in drinking
water based on new information that indicates that perchlorate does not
occur in public water systems with a frequency and at levels of public
health concern and there may not be a meaningful opportunity for health
risk reduction through a drinking water regulation. Under this last
alternative, the final action would be a withdrawal of the
determination to regulate and there would be no MCLG or national
primary drinking water regulation for perchlorate. This proposed rule
is organized as follows:

I. General Information
    A. What is the EPA proposing?
    B. Does this action apply to me?
II. Background
    A. What is perchlorate?
    B. Statutory Authority
    C. Statutory Framework and Regulatory History
III. Assessment and Modeling of the Health Effects of Perchlorate
    A. 2008 Preliminary Regulatory Determinations
    B. 2009 Supplemental Request for Comment and 2011 Final
Regulatory Determination
    C. Science Advisory Board Recommendations
    D. Perchlorate Model Development and Peer Reviews
    E. Sensitive Population for Deriving MCLG
    F. BBDR Model Specification for the Sensitive Population
    G. Epidemiological Literature
    H. Identifying a Point of Departure for Developing the MCLG
    I. Translate PODs to RfDs
    J. Translate RfD Into an MCLG
IV. Maximum Contaminant Level Goal and Alternatives
V. Maximum Contaminant Level and Alternatives
VI. Occurrence
VII. Analytical Methods
VIII. Monitoring and Compliance Requirements
    A. What are the proposed monitoring requirements?
    B. Can States grant monitoring waivers?
    C. How are system MCL violations determined?
    D. When must systems complete initial monitoring?
    E. Can systems use grandfathered data to satisfy the initial
monitoring requirements?
IX. Safe Drinking Water Act Right to Know Requirements
    A. What are the Consumer Confidence Report requirements?
    B. What are the public notification requirements?
X. Treatment Technologies
    A. What are the best available technologies?
    B. What are the small system compliance technologies?
XI. Rule Implementation and Enforcement
    A. What are the requirements for primacy?
    B. What are the State record keeping requirements?
    C. What are the State reporting requirements?
XII. Health Risk Reduction Cost Analysis
    A. Identifying Affected Entities
    B. Method for Estimating Costs
    C. Method for Estimating Benefits
    D. Comparison of Costs and Benefits
XIII. Uncertainty Analysis
    A. Uncertainty in the MCLG Derivation
    B. Uncertainty in the Economic Analysis
XIV. Request for Comment on Proposed Rule
XV. Request for Comment on Potential Regulatory Determination
Withdrawal
XVI. Statutory and Executive Order Reviews
    A. Executive Order 12866: Regulatory Planning and Review and
Executive Order 13563 Improving Regulation and Regulatory Review
    B. Executive Order 13771: Reducing Regulations and Controlling
Regulatory Costs
    C. Paperwork Reduction Act
    D. Regulatory Flexibility Act (RFA)
    E. Unfunded Mandates Reform Act
    F. Executive Order 13132: Federalism
    G. Executive Order 13175: Consultation and Coordination With
Indian Tribal Governments
    H. Executive Order 13045: Protection of Children From
Environmental Health and Safety Risks
    I. Executive Order 13211: Actions That Significantly Affect
Energy Supply, Distribution, or Use
    J. National Technology Transfer and Advancement Act of 1995

    K. Executive Order 12898: Federal Actions To Address
Environmental Justice in Minority Populations and Low-Income
Populations
XVII. Consultations with the Science Advisory Board, National
Drinking Water Advisory Council, and the Secretary of Health and
Human Services
XVIII. References

I. General Information

A. What is the EPA proposing?

    This action contains a proposal and three alternatives for public
comment. First, the EPA proposes to establish a Maximum Contaminant
Level Goal (MCLG) and National Primary Drinking Water Regulation
(NPDWR) for perchlorate in public water supplies. The EPA proposes an
MCLG of 56 [micro]g/L, and to regulate perchlorate in drinking water at
an enforceable maximum contaminant level (MCL) of 56 [micro]g/L.
    The EPA is proposing an NPDWR for perchlorate in accordance with
its February 11, 2011, (76 FR 7762) determination to regulate
perchlorate under the SDWA. Based on the best available peer reviewed
science at that time, the EPA found that perchlorate met the SDWA's
three criteria for regulating a contaminant: (1) The contaminant may
have an adverse effect on the health of persons, (2) the contaminant is
known to occur or there is a substantial likelihood that the
contaminant will occur in public water systems (PWSs) with a frequency
and at levels of public health concern, and (3) in the sole judgment of
the Administrator, regulation of such contaminant presents a meaningful
opportunity for health risk reduction for persons served by PWSs.
    Second, as explained in more detail below, the EPA is soliciting
comment on two alternative MCLG/MCL values of 18 [micro]g/L and 90
[micro]g/L respectively. Third, in light of new considerations that
have come to the EPA's attention since it issued its positive
regulatory determination in 2011, including information on lower levels
of occurrence of perchlorate than the EPA had previously believed to
exist and new analysis of the concentration that represents a level of
health concern, this action also discusses and requests comment on an
alternative action under which the EPA would withdraw its 2011
determination to regulate perchlorate. Under this alternative, there
would be no MCLG or NPDWR for perchlorate.

B. Does this action apply to me?

    Entities that could potentially be affected include the following:

------------------------------------------------------------------------
           Category            Examples of potentially affected entities
------------------------------------------------------------------------
Public water systems.........  Community water systems: Non-transient,
                                non-community water systems.
State and tribal agencies....  Agencies responsible for drinking water
                                regulatory development and enforcement.
------------------------------------------------------------------------

    This table is not intended to be exhaustive, but rather provides a
guide for readers regarding entities that could be affected by this
action. To determine whether your facility or activities could be
affected by this action, you should carefully examine this proposed
rule. If you have questions regarding the applicability of this action
to a particular entity, consult the person listed in the FOR FURTHER
INFORMATION CONTACT section.

II. Background

A. What is perchlorate?

    Perchlorate is a negatively charged inorganic ion that is comprised
of one chlorine atom bound to four oxygen atoms (ClO4-),
which is highly stable and mobile in the aqueous environment.
Perchlorate comes from both natural and manmade sources. It is formed
naturally via atmospheric processes and can be found within mineral
deposits in certain geographical areas. It is also produced in the
United States, and the most common compounds include ammonium
perchlorate and potassium perchlorate used primarily as oxidizers in
solid fuels to power rockets, missiles, and fireworks. For the general
population, most perchlorate exposure is through the ingestion of
contaminated food or drinking water.

B. Statutory Authority

    Section 1412(b)(1)(A) of the SDWA requires the EPA to establish
NPDWRs for contaminants that may have an adverse effect on the health
of persons; that are known to occur or there is a substantial
likelihood that the contaminant will occur in public water systems with
a frequency and at levels of public health concern; and where in the
sole judgment of the Administrator, regulation of such contaminant
presents a meaningful opportunity for health risk reduction for persons
served by public water systems.

C. Statutory Framework and Regulatory History

    Section 1412(b)(1)(B)(i) of the SDWA requires the EPA to publish
every five years a Contaminant Candidate List (CCL). The CCL is a list
of drinking water contaminants that are known or anticipated to occur
in public water systems and are not currently subject to the EPA
drinking water regulations. The EPA uses the CCL to identify priority
contaminants for regulatory decision-making and information collection.
Contaminants listed on the CCL may require future regulation under the
SDWA. The EPA included perchlorate on the first, second, and third CCLs
published in 1998, 2005, and 2009.
    Once listed on the CCL, the Agency continues to collect data on CCL
contaminants to better understand their potential health effects and to
determine the levels at which they occur in drinking water. Section
1412(b)(1)(B)(ii) requires that, every five years, the EPA, after
public comment, issue a determination whether or not to regulate at
least five contaminants on the CCL. For any contaminant that the EPA
determines meets the criteria for regulation, under Section
1412(b)(1)(E), the EPA must issue a proposed national primary drinking
water regulation within two years and issue a final regulation 18
months after the proposal (which may be extended by 9 months).
    As part of its responsibilities under the SDWA, the EPA implements
section 1445(a)(2), ``Monitoring Program for Unregulated
Contaminants.'' This section requires that once every five years, the
EPA issue a list of no more than 30 unregulated contaminants to be
monitored by public water system. This monitoring is implemented
through the Unregulated Contaminant Monitoring Rule (UCMR), which
collects data from community water systems (CWS) and non-transient,
non-community water systems (NTNCWS). The UCMR collects data from a
census of large water systems (serving more than 10,000 people) and
from a statistically representative sample of small water systems. On
September 17, 1999, the EPA published its first UCMR (64 FR 50556)
which required all large systems and a representative sample of small
systems to monitor for perchlorate and 25 other contaminants (USEPA,
1999, 2000b).
    The EPA and other federal agencies asked the National Research
Council

(NRC) to evaluate the health implications of perchlorate ingestion. The
NRC concluded that perchlorate exposure inhibits the transport of
iodide \1\ into the thyroid by a protein molecule knows as the sodium/
iodide symporter (NIS), which may lead to decreases in two hormones,
thyroxine (T3) and triiodothyronine (T4) and increases in thyroid-
stimulating hormone (TSH) (National Research Council (NRC), 2005b).
Additionally, the NRC concluded that the most sensitive population to
perchlorate exposure are ``the fetuses of pregnant women who might have
hypothyroidism or iodide deficiency'' (p. 178). The EPA established a
reference dose (RfD) consistent with the recommended National Research
Council RfD of 0.7 [micro]g/kg/day for perchlorate. The reference dose
is an estimate of a daily exposure to humans that is likely to be
without an appreciable risk of adverse effects. This RfD was based on a
study (Greer, Goodman, Pleus, & Greer, 2002) of perchlorate's
inhibition of radioactive iodine uptake in healthy adults and the
application of an uncertainty factor of 10 for intraspecies variability
(USEPA, 2005b).
---------------------------------------------------------------------------

    \1\ For the purposes of this FRN, ``iodine'' will be used to
refer to dietary intake before entering the body. Once in the body,
``iodide'' will be used to refer to the ionic form.
---------------------------------------------------------------------------

    In October 2008, the EPA published a preliminary regulatory
determination not to regulate perchlorate in drinking water and
requested public comment (73 FR 60262). In that preliminary
determination, the EPA tentatively concluded that perchlorate did not
occur with a frequency and at levels of public health concern and that
development of a regulation did not present a meaningful opportunity
for health risk reduction for persons served by public water systems.
The EPA derived and used a Health Reference Level (HRL) of 15 [mu]g/L
based on the RfD of 0.7 [micro]g/kg/day in making this conclusion
(USEPA, 2008a). Based primarily on the UCMR 1 occurrence data, the EPA
estimated that less than 1% of drinking water systems (serving
approximately 1 million people) had perchlorate levels above the HRL of
15 [micro]g/L. Based on this information the Agency determined that
perchlorate did not occur frequently at levels of health concern. The
EPA also determined that there was not a meaningful opportunity for a
NPDWR to reduce health risks.
    In January 2009 the EPA published an interim health advisory for
perchlorate of 15 [micro]g/L, consistent with the HRL derivation for
perchlorate of 15 [micro]g/L described above. Health Advisories are
non-enforceable and non-regulatory and provide technical information to
state agencies and other public health officials on health effects,
analytical methodologies, and treatment technologies associated with
drinking water contamination. Health Advisories provide the public,
including the most sensitive populations, with a margin of protection
from a lifetime of exposure. For perchlorate, the health advisory was
developed for subchronic exposure (USEPA 2008d).
    In August 2009, the EPA published a supplemental request for
comment with a new analysis that derived potential alternative HRLs for
14 life stages, including infants and children. The analysis used the
RfD of 0.7 [mu]g/kg/day and life stage-specific bodyweight and exposure
information (74 FR 41883; USEPA, 2009a). After careful consideration of
public comments on the October 2008 and August 2009 notices, on
February 11, 2011, the EPA published its determination to regulate
perchlorate (76 FR 7762; USEPA, 2011a). The Agency stated then that
when considering the alternative HRL benchmarks described in the 2009
notice, the likelihood of perchlorate to occur at levels of concern had
significantly increased in comparison to the levels described on the
2008 preliminary negative determination. The EPA concluded that as many
as 16 million people could potentially be exposed to perchlorate at
levels of concern, up from 1 million people originally described in the
2008 notice.
    In its 2011 determination, the Agency found that perchlorate may
have an adverse effect on the health of persons, that it is known to
occur in public drinking water systems with a frequency and at levels
that present a public health concern, and in the judgment of the
Administrator, regulation of perchlorate presented a meaningful
opportunity for health risk reduction for persons served by public
water systems. As a result of the determination, and as required by
Section 1412(b)(1)(E), the EPA initiated the process to develop an MCLG
and NPDWR for perchlorate as described in this notice.
    In September 2012, the U.S. Chamber of Commerce (the Chamber)
submitted to the EPA a Request for Correction under the Information
Quality Act regarding the EPA's regulatory determination. In the
request, the Chamber claimed that the UCMR 1 data did not comply with
data quality guidelines and were not representative of current
conditions. In response to this request, the EPA reassessed the data
and removed certain source water samples that could be paired with
appropriate follow-up samples located at the entry point to the
distribution system. The EPA also updated the UCMR 1 data for systems
in California and Massachusetts using state compliance data to reflect
current occurrence conditions after state regulatory limits for
perchlorate were implemented.
    In response to a lawsuit brought to enforce the deadlines in
Section 1412(b)(1)(E), the U.S. District Court for the Southern
District of New York entered a consent decree, requiring the EPA to
propose an NPDWR with a proposed MCLG for perchlorate in drinking water
no later than October 31, 2018, and finalize an NPDWR and MCLG for
perchlorate in drinking water no later than December 19, 2019. The
deadline for the EPA to propose an NPDWR with a proposed MCLG for
perchlorate in drinking water was later extended to May 28, 2019. The
consent decree is available in the docket for today's proposed rule.

III. Assessment and Modeling of the Health Effects of Perchlorate

    Perchlorate inhibits uptake of iodide into the thyroid gland by
competitively binding to the NIS (ATSDR, 2008; Greer et al., 2002; NRC,
2005; SAB 2013; Taylor et al., 2013). Iodide is necessary for the
synthesis of thyroid hormones and decreased iodide uptake into the
thyroid can adversely affect thyroid hormone production (SAB for the
U.S. EPA, 2013; Blount et al., 2006; Steinmaus et al., 2007, 2013,
2016, McMullen et al., 2017; Knight et al., 2018). These changes in
thyroid hormone levels in a pregnant woman may be linked to changes in
the neurodevelopment of her offspring (SAB for the U.S. EPA, 2013;
Korevaar et al., 2016; Fan and Wu, 2016; Wang et al., 2016; Alexander
et al., 2017; Thompson et al., 2018). In addition, alterations in
thyroid homeostasis may impact other body systems including the
reproductive (Alexander et al., 2017; Hou et al., 2016; Maraka et al.,
2016) and cardiovascular systems (Asvold et al., 2012; Sun et al.,
2017).
    The mode of action of perchlorate toxicity has been proposed as
follows: exposure to perchlorate is known to inhibit the uptake of
iodide by the thyroid gland through the NIS (NRC, 2005; SAB for the
U.S. EPA, 2013). A sufficient inhibition of iodide uptake results in
iodide deficiency within the thyroid. Given that T3 and T4 require
iodide for production, a decrease in intra-thyroidal iodide can result
in decreased production of these hormones. This could in turn result in
increased TSH, the hormone that acts on

the thyroid gland to stimulate iodide uptake to increase thyroid
hormone production (Blount, Pirkle, Osterloh, Valentin-Blasini, &
Caldwell, 2006; National Research Council (NRC), 2005; Steinmaus,
Miller, Cushing, Blount, & Smith, 2013; Steinmaus et al., 2016). For
populations with developing brains (e.g., fetuses, neonates, and
children), disruptions in homeostatic thyroid hormone function can
result in adverse neurodevelopmental effects (Alexander et al., 2017;
Glinoer & Delange, 2000; Glinoer & Rovet, 2009; SAB for the U.S. EPA,
2013). Specifically, decreased maternal thyroid hormone levels during
pregnancy, including in the hypothyroxinemic range,\2\ have been linked
to decrements in neurocognitive function in offspring (Alexander et
al., 2017; Thompson et al., 2018; Wang et al., 2016). There is also
limited evidence to suggest an association with other adverse
neurodevelopmental outcomes including ADHD, expressive language delay,
reduced school performance, autism, and delayed cognitive development
(Alexander et al., 2017; Ghassabian, Bongers-Schokking, Henrichs,
Jaddoe, & Visser, 2011; Gyllenberg et al., 2016; Henrichs et al., 2010;
Korevaar et al., 2016, Noten et al., 2015; Pop et al., 2003, 1999; SAB
for the U.S. EPA, 2013; van Mil et al., 2012).
---------------------------------------------------------------------------

    \2\ Maternal hypothyroxinemia is defined as TSH in the reference
range and fT4 in the lower percentiles. The SAB notes that
hypothyroxinemia has been defined by a ``variety of cutoffs . . .
ranging from fT4 below the 10th or 5th percentiles to below the
2.5th percentile'' (SAB, 2013, p.10) in the population.
---------------------------------------------------------------------------

    The difficulty in estimating the likelihood and magnitude of the
potential implications of perchlorate's mode of action on expressed
neurodevelopmental health effects in humans exposed to perchlorate
during development is the lack of robust epidemiological studies,
especially in sensitive populations. Therefore, based on the known mode
of action of perchlorate the Agency estimated potential health risks
using a novel approach suggested by the EPA's Science Advisory Board
(SAB for the U.S. EPA, 2013). The EPA's approach to estimating
perchlorate risks has evolved over time with improved research and
modeling capabilities. The following sections describe information
sources the EPA used in its assessment as well as the regulatory
process followed by the Agency in its decision making.

A. 2008 Preliminary Regulatory Determinations

    In 2005, at the request of the EPA and other federal agencies, the
NRC evaluated the health implications of perchlorate ingestion. The NRC
concluded that perchlorate exposure could inhibit the transport of
iodide into the thyroid, leading to thyroid hormone deficiency (NRC,
2005). A significant inhibition of iodide uptake results in intra-
thyroid iodide deficiency, decreased synthesis of T3 and T4, and
increased TSH. The NRC also concluded that a prolonged decrease of
thyroid hormones is potentially more likely to have adverse effects in
sensitive populations (e.g., the fetuses of pregnant women who might
have hypothyroidism or iodide deficiency). Based on these findings, the
NRC recommended a reference dose of 0.7 [micro]g/kg/day.
    Based on NRC's analysis, the EPA established a perchlorate
reference dose (RfD) of 0.7 [micro]g/kg/day in 2005 (USEPA, 2005). This
value was based on a no observed effect level (NOEL) of 7 [micro]g/kg/
day identified from a study (Greer, Goodman, Pleus, & Greer, 2002) of
perchlorate's inhibition of radioactive iodine uptake in healthy adults
and the application of an uncertainty factor of 10 for intraspecies
variability.
    As discussed above, in 2008, the EPA derived an HRL of 15 [mu]g/L
using the RfD of 0.7 [mu]g/kg/day, a default bodyweight of 70 kg, a
default drinking water consumption rate of 2 L/day, and a perchlorate-
specific relative source contribution (RSC) of 62 percent that was
derived for a pregnant woman (USEPA, 2008a) (73 FR 60262). The RSC is
the percentage of the RfD remaining for drinking water after other
sources of exposure to perchlorate (i.e., food) have been considered.
The EPA's HRL was calculated to offer a margin of protection against
adverse health effects to the subpopulation identified by the NAS as
likely the most sensitive to the effects of perchlorate exposure,
fetuses.

B. 2009 Supplemental Request for Comment and 2011 Final Regulatory
Determination

    The EPA received over 33,000 comments in response to its 2008
preliminary determination to not regulate perchlorate (USEPA, 2011a).
After reviewing the comments, the EPA developed alternative HRLs for
other sensitive populations in addition to fetuses of pregnant women.
The EPA developed alternative HRLs for 14 life stages including infants
and children. The EPA also evaluated the occurrence of perchlorate at
levels above these alternative HRLs using the UCMR 1 occurrence data.
    The analysis used the RfD of 0.7 [mu]g/kg/day and life stage-
specific bodyweight and exposure information (i.e., drinking water
intake, RSC) for each of the 14 life stages evaluated. The resulting
HRLs ranged from 1 [mu]g/L to 47 [mu]g/L. In August 2009, the EPA
published a supplemental request for comment with the new analysis and
HRLs (74 FR 41883; USEPA, 2009a). After careful consideration of public
comments, on February 11, 2011, the EPA published its final
determination to regulate perchlorate (76 FR 7762; USEPA, 2011a).

C. Science Advisory Board Recommendations

    As required by Section 1412(d) of the SDWA, as part of the NPDWR
development process, the EPA requested comments from the Science
Advisory Board (SAB) in 2012, seeking guidance on how best to consider
and interpret the life stage information, the epidemiologic and
biomonitoring data since the NRC report, physiologically-based
pharmacokinetic (PBPK) analyses, and the totality of perchlorate health
information to derive an MCLG for perchlorate. The SAB recommended the
following:
     Derive a perchlorate MCLG that addresses sensitive life
stages through physiologically based pharmacokinetic/pharmacodynamic
(PBPK/PD) modeling based upon perchlorate's mode of action rather than
the default MCLG approach using the RfD and specific chemical exposure
parameters;
     expand the modeling approach to account for thyroid
hormone perturbations and potential adverse neurodevelopmental outcomes
from perchlorate exposure;
     utilize a mode-of-action framework for developing the MCLG
that links the steps in the proposed mechanism leading from perchlorate
exposure through iodide uptake inhibition--to thyroid hormone changes--
and finally to neurodevelopmental impacts; and
     ``Extend the [BBDR] model expeditiously to . . . provide a
key tool for linking early events with subsequent events as reported in
the scientific and clinical literature on iodide deficiency, changes in
thyroid hormone levels, and their relationship to neurodevelopmental
outcomes during sensitive early life stages'' (SAB for the U.S. EPA,
2013, p. 19).
    This SAB-proposed framework would incorporate the previous endpoint
of iodide uptake inhibition that was the basis for the RfD as part of a
broader and more comprehensive framework that links perchlorate
exposure to adverse neurodevelopmental outcomes. It also focuses on the
smaller changes in thyroid hormones (specifically free T4 (fT4)) that
are associated with maternal hypothyroxinemia and subsequent

adverse neurodevelopmental health effects rather than the significant
changes in thyroid hormones (both fT4 and TSH) that are associated with
hypothyroidism.

D. Perchlorate Model Development and Peer Reviews

    To address the SAB recommendations, the EPA revised an existing
PBPK/PD model that describes the dynamics of perchlorate, iodide, and
thyroid hormones in a woman during the third trimester of pregnancy
(Lumen, Mattie, & Fisher, 2013; USEPA, 2009b). The EPA also created its
own Biologically Based Dose Response (BBDR) models that included the
additional sensitive life stages identified by the SAB, i.e., breast-
and bottle-fed neonates and infants (SAB for the U.S. EPA, 2013, p.
19).
    To determine whether the Agency had implemented the SAB
recommendations for modeling thyroid hormone changes, the EPA convened
an independent peer review panel to evaluate the BBDR models in January
2017 (External Peer Reviewers for USEPA, 2017). In addition to
estimating effects on breast fed infants, several reviewers recommended
that the EPA shift the primary focus of its analysis to modeling the
exposure implications to the fetus during early pregnancy. This was
based on the knowledge that fetuses lack a functioning thyroid gland
until approximately 16 gestational weeks and the substantial
epidemiological evidence linking early pregnancy low fT4 levels with
adverse neurodevelopmental outcomes (Endendijk et al., 2017, Korevaar
et al., 2016; Morreale de Escobar, Obreg[oacute]n, & Escobar del Rey,
2004, Pop et al., 1999; Pop et al., 2003). Specifically, the SAB
recommended that the EPA use specific sensitive populations to develop
the MCLG for perchlorate: ``the fetuses of hypothyroxinemic pregnant
women, and infants exposed to perchlorate through either water-based
formula preparations or the breast milk of lactating women'' (SAB for
the U.S. EPA, 2013, p. 19).
    The EPA considered all recommendations from the 2017 peer review.
The previously developed BBDR model describing perchlorate's effects in
the third trimester (Lumen, Mattie, & Fisher, 2013; USEPA, 2009b) was
calibrated only for that phase of pregnancy, not for the first
trimester, and lacked a description of TSH signaling (feedback) that
becomes significant as individuals become hypothyroxinemic or
hypothyroid. In particular, this signaling was considered necessary to
accurately predict responses of women with very low iodine intake,
which was also part of the 2017 peer review recommendations. Therefore,
the Lumen et al., (2009b) model needed to be revised to address these
recommendations and the EPA implemented those changes needed to
increase the scientific rigor of the model and modeling results. These
modifications include:
     Extending the model to early pregnancy;
     Incorporating biological feedback control of hormone
production via TSH signaling, such that the model can describe lower
levels of iodide nutrition;
     Calibrating the model and evaluating its behavior for
upper and lower percentiles of the population, as well as the
population median; and
     Conducting an uncertainty analysis for key parameters.
    The EPA convened a second independent peer review panel in January
2018 to evaluate these updates to the BBDR model. The EPA also
presented several approaches in the draft Proposed Approaches to Inform
the Derivation of a Maximum Contaminant Level Goal for Perchlorate in
Drinking Water (MCLG Approaches Report) to link the thyroid hormone
changes in a pregnant mother predicted by the BBDR model to
neurodevelopmental effects using evidence from the epidemiological
literature (External Peer Review for U.S. EPA, 2018). The 2018 peer
review identified a variety of strengths and limitations of the
modeling (to be discussed in more detail later in this notice). The
peer review panel was largely supportive of the efforts described in
the MCLG Approaches Report, as evidenced by the following from the peer
review final report:
    Overall, the panel agreed that the EPA and its collaborators have
prepared a highly innovative state-of-the-science set of quantitative
tools to evaluate neurodevelopmental effects that could arise from
drinking water exposure to perchlorate. While there is always room for
improvement of the models, with limited additional work to address the
committee's comments [in the peer-reviewed report], the current models
are fit-for-purpose to determine an MCLG (External Peer Reviewers for
U.S. EPA, 2018, p. 2).
    The EPA also presented an alternative, population-based approach
evaluating the shift in the proportion of the population that would
fall below a hypothyroxinemic cut point, given exposure to perchlorate
(Section 7 of the MCLG Approaches Report). This approach does not
directly connect the BBDR output to a neurodevelopmental endpoint.
However, for pregnant women in early pregnancy, this shift could be
related to avoiding an increase in the population of offspring's risk
of adverse neurodevelopmental impacts. The 2018 peer review identified
strengths associated with this approach, including
    (1) the central premise, that hypothyroxinemia is associated with
adverse neurodevelopmental effects is supported by a large number of
studies, including categorical studies; (2) this approach encompasses a
variety of adverse neurodevelopmental outcomes, as indicated by these
studies, rather than focusing on one or a limited number of adverse
outcomes, as with the two-stage approach; and (3) this approach avoids
all of the uncertainties associated with determining a quantitative
relationship between a specific maternal fT4 level and the magnitude an
adverse neurodevelopmental effect. (External Peer Reviewers for U.S.
EPA, 2018, p. 7)
    The peer reviewers expressed concern about hypothyroxinemia being a
precursor effect, rather than an adverse health outcome, which they
argued may create difficulties in explaining the basis for an MCLG
based on this approach to some audiences. However, the EPA has used
precursor effects as the basis for setting regulatory and non-
regulatory limits previously. The peer-review panel also expressed
concern that a standard definition of hypothyroxinemia has not yet been
established, as clinicians use varying fT4 thresholds to define their
own working definition of the condition. This also could lead to
difficulties communicating the population at risk for developing this
precursor effect as a result of perchlorate exposure.
    Ultimately, the EPA chose to develop the MCLG using dose-response
functions from the epidemiological literature to estimate
neurodevelopmental impacts in the offspring of pregnant women exposed
to perchlorate. The EPA selected this proposed approach because it is
consistent with the SDWA's definition of an MCLG to avoid adverse
health effects and because it is most consistent with the SAB
recommendations. The EPA is requesting public comment in Section XIV on
the adequacies and uncertainties of the methodology to derive the MCLG
including the decision not to pursue this population-based approach for
setting the MCLG.
    Based on the comments of the peer reviewers, the EPA's final
analysis informing the derivation of the MCLG and benefits of avoided
perchlorate exposure is based upon a 2-step

approach to modeling the neurodevelopmental effects on offspring of
pregnant women exposed to perchlorate in drinking water (see Figure 1).
In summary, because of the known mode of action, the lack of
epidemiological studies particularly in the sensitive populations and
the direction of the SAB to use a ``data-driven approach [which]
represents a more rigorous way to address differences in biology and
exposure between adults and sensitive life stages'' (p. 2, SAB 2013 for
U.S. EPA), the EPA uses a combination of the BBDR model that simulates
perchlorate potential impacts on maternal thyroid hormones during
pregnancy and the epidemiology literature that relates incremental
changes in maternal thyroid hormones to neurodevelopmental outcomes in
children. The following sections describe the approach in greater
detail, highlighting each step in which decisions and assumptions were
made.
[GRAPHIC] [TIFF OMITTED] TP26JN19.008

    Note: Process figure does not imply the strength of scientific
evidence.

E. Sensitive Population for Deriving MCLG

    SDWA 1412(b)(4)(A) requires MCLGs to be set at a concentration in
water ``at which no known or anticipated adverse effects on the health
of persons occur and which allows an adequate margin of safety.'' SDWA
1412(b)(3)(C)(V) further requires that the EPA ``consider the effects
of the contaminant on the general population and on groups within the
general population such as infants, children, pregnant women, the
elderly, individuals with a history of serious illness, or other
subpopulations that are identified as likely to be at greater risk of
adverse health effects due to exposure to contaminants in drinking
water than the general population.'' The EPA has interpreted these
requirements to establish MCLGs that avoid adverse effects within the
portions of the population that are at greater risk of adverse effects
from exposure to the contaminant. The EPA is proposing an MCLG that is
developed to protect the fetuses of a first trimester pregnant mother
with low-iodine intake levels (i.e., 75 [micro]g/kg/day), low fT4
levels (i.e., 10th percentile of an fT4 distribution for individuals
with 75 [micro]g/day iodine intake), and weak TSH feedback strength
(i.e., TSH feedback is reduced to be approximately 60 percent less
effective than for the median individual). The choice of this
population is consistent with discussion by the NRC (2005), and the SAB
(2013). The EPA believes that by protecting this population, the other
sensitive populations (i.e., breast- and bottle-fed infants) will also
be protected. This conclusion is based on the EPA's analysis of
predictions of the impact of perchlorate on fT4 levels from the
original EPA BBDR model (which was peer reviewed in January of 2017)
and an analysis of the literature on the connection between altered
thyroid hormones in these life stages, and neurodevelopmental outcomes.
    The EPA's original BBDR model demonstrated that perchlorate had
minimal impact on the thyroid hormone levels for 30-, 60-, and 90-day
formula-fed infants, even at doses as high as 20 [micro]g/kg/day.
Specifically, the model demonstrated that ``the range of iodine levels
in formula is sufficient to almost entirely offset the effects of
perchlorate exposure at 30, 60 and 90 days'' (USEPA, 2017; p. 73). As a
result of these findings the EPA concluded that any MCLG based on the
fetus of the first trimester hypothyroxinemic pregnant mother would
also protect the formula-fed infant.
    To determine if the same would be true for the breast-fed infant,
the EPA compared the predicted percent change in fT4 experienced at
given doses of perchlorate for both the breast-fed infant and the first
trimester pregnant mother at varying doses of iodine intake \3\ (50 to
100 [micro]g/day). Assuming 2 or 4 [micro]g/kg/day of perchlorate, the
first trimester hypothyroxinemic pregnant mother has a greater percent
change in fT4 compared to the 30 and 60 day breast-fed infant at all
maternal iodine intake levels evaluated, except for the 30 day breast-
fed infant of a mother consuming only 50 [micro]g/day iodine. However,
given that the original BBDR model did not have a TSH feedback loop,
T4, fT4, T3 and fT3 predictions for lactating mothers with less than 75
[micro]g/day iodine intake were considered highly uncertain because the
thyroid hormone levels had fallen into the hypothyroid range.
---------------------------------------------------------------------------

    \3\ Given that the current version of the BBDR model contains a
TSH feedback loop and the infant models previously developed did not
contain this feedback loop, this comparison is done with the
feedback loop turned off.
---------------------------------------------------------------------------

    The Agency found that there are reports in the scientific
literature suggesting that minor perturbations in thyroid hormone
levels in the first trimester mother may adversely impact her
offspring's neurodevelopment. Specifically, some studies show that
children exposed gestationally to maternal hypothyroxinemia (without
hypothyroidism) have a higher risk of reduced levels of global and
specific cognitive abilities, as well as increased rates of behavior
problems including greater dysregulation in early infancy and
attentional disorders in childhood (Kooistra, Crawford, van Baar,
Brouwers, & Pop, 2006; Man, Brown, & Serunian, 1991; Pop et al., 2003;
Pop et al., 1999). Notably these effects are correlated with both
degree (Henrichs et al., 2010; Pop et al., 1999) and duration (Pop et
al., 2003) of maternal hypothyroxinemia (SAB for the U.S. EPA, 2013, p.
10).
    The EPA did not find analogous evidence linking minor perturbations
in thyroid hormones during infancy to adverse neurodevelopmental
outcomes in infants. This finding is consistent with conclusions by the
California Environmental Protection Agency (CalEPA) in their assessment
of a public health goal for perchlorate (California Environmental
Protection Agency, 2011, p. 90).
    Specifically, two studies evaluated both the impact of maternal
hypothyroxinemia and infant fT4 levels on subsequent neurodevelopmental
outcomes. Costeira et al. (2011) found that children born to mothers
with low fT4 in the first trimester had increased odds of mild-to-
severe delays in

psychomotor development compared to children born to mothers with
normal fT4 levels. However, the authors found that neonatal thyroid
status (measured on day 3 after birth) did not influence development.
Additionally, Henrichs et al. (2010) found in their evaluation that
although maternal hypothyroxinemia was associated with language delay
and nonverbal cognitive delay, the neonatal thyroid status (thyroid
hormones measured in cord blood) did not explain the relationship
between maternal hypothyroxinemia, early pregnancy, and children's
cognitive impairment.
    The SAB pointed to two lines of evidence supporting their
suggestion of the infant as a potentially sensitive population to
perchlorate: Preterm infants that experience transient hypothyroxinemia
of prematurity (THOP) and infants that experience congenital
hypothyroidism (SAB for the U.S. EPA, 2013). Thus, sufficient thyroid
hormone levels in infancy are necessary for the infant brain to develop
properly. However, the best evidence linking perturbations in thyroid
hormone levels to disrupted neurodevelopment for infants are in
individuals with significant thyroid deficiencies manifesting as
clinical conditions (e.g., THOP and congenital hypothyroidism). It is
unclear and unknown if minor perturbations in thyroid hormones in
infants, such as those that could be caused by environmental levels of
perchlorate, would result in adverse neurodevelopmental outcomes
similar to those seen in the literature for the offspring of first
trimester pregnant mothers with hypothyroxinemia. Given the lack of
evidence demonstrating minor perturbations in infant fT4 levels as
being associated with neurodevelopmental outcomes, the EPA has
concluded that it is appropriate to derive the perchlorate MCLG to
protect the first trimester fetus of a pregnant mother with low-iodine
intake. The EPA concludes that an MCLG calculated to offer a margin of
protection against adverse health effects to these fetuses targets the
most sensitive lifestage and will be protective of other potentially
sensitive life stages as well.

F. BBDR Model Specification for the Sensitive Population

    The BBDR model used to develop the proposed MCLG has two main
components:
     A pharmacokinetic model for perchlorate and iodide, which
describes chemical absorption, distribution, metabolism, and excretion
of perchlorate and iodide; and
     A pharmacodynamic model, which describes the joint effect
of varying perchlorate and iodide blood concentrations on thyroidal
uptake of iodide and subsequent production of thyroid hormones,
including fT4.
    The pharmacokinetic model component contains a physiological
description of a human mother and fetus during pregnancy (e.g., organ
volumes, blood flows) and chemical-specific information (e.g.,
partition coefficients, volume of distribution, rate constants for
transport, metabolism, and elimination) that enable a prediction of
perchlorate and iodide internal concentration at the critical target
(i.e., thyroidal sodium-iodide symporter of the mother) in association
with a particular exposure scenario (route of exposure, age, dose
level). This component of the model is similar to many other PBPK
models. Because perchlorate does not undergo metabolism in vivo
(Clewell et al., 2007), potential uncertainty from this factor of the
model is avoided since it does not need to be described.
    The pharmacodynamic component of the model uses this internal
concentration to simulate how the chemical will act within a known
mechanism of action to perturb host systems and lead to a toxic effect.
    Thus, the BBDR model estimates serum thyroid hormone levels in the
mother at specific gestational weeks, given specific levels of iodine
intake, the TSH feedback loop strength, and perchlorate doses. As noted
above, to be health protective the EPA chose to model a sensitive
individual (an adult woman with low iodine through the first trimester
of pregnancy) to derive an MCLG, thereby protecting both this target
sensitive population with an adequate margin of safety and those who
are less sensitive with an even larger margin of safety.
    The BBDR model simulates perchlorate's impact on thyroid hormones
at each gestational week from conception to week 16. To derive the
MCLG, the EPA selected outputs for gestational week 13 to correspond
with the thyroid hormone data reported in Korevaar et al., (2016),
which is the basis for the Agency's quantitative relationship between
maternal thyroid hormone levels and neurodevelopmental impacts.
    Individuals with low iodine intake have increased sensitivity to
perchlorate's impact on thyroid hormone levels because the functional
iodide reserve of the hypothalamic-pituitary-thyroid (HPT) system is
limited (Blount et al., 2006, Steinmaus et al., 2007; Leung, Pearce, &
Braverman, 2010). The EPA selected an iodine intake level of 75
[micro]g/day to simulate an individual with low-iodine intake. This
value represents an intake between the 15th and 20th percentile of the
women of child bearing age population distribution of estimated iodine
intake from the National Health and Nutrition Examination Survey
(NHANES). The EPA considered using a lower iodine intake level of 50
[micro]g/day, which represents approximately the 5th percentile of the
NHANES distribution. At 50 [micro]g/day of iodine intake, however, the
BBDR model predicts TSH levels that would be elevated to within the
clinically hypothyroid range before exposure to any perchlorate \4\
(TSH ranges between 4.51 and 5.41 milli-international units per liter
(mIU/L) at zero dose of perchlorate when evaluating gestational weeks
12 or 13). In contrast, at 75 [micro]g/day iodine, the BBDR modeled
concentrations of serum fT4 and TSH are significantly reduced from the
population median but are still within the euthyroid range. Thus, the
intake of 75 [micro]g/day is a better approximation of the sensitive
population--the offspring of pregnant women who have low fT4.
---------------------------------------------------------------------------

    \4\ For the purposes of this analysis, the EPA evaluated the
American Thyroid Association's (ATA's) 2017 recommendations for
defining hypothyroidism (Alexander et al., 2017). Specifically the
ATA recommends ``in the pregnancy setting, maternal hypothyroidism
is defined as a TSH concentration elevated beyond the upper limit of
the pregnancy-specific reference range'' (Alexander et al., 2017, p.
332). ATA goes on to state, in the absence of population- and
trimester-specific reference ranges defined by a provider's
institute or laboratory, that the TSH reference ranges should be
obtained from similar patient populations. From their recommended
studies with trimester-specific data on a U.S. population, Lambert-
Meserlian et al. (2008) is the largest U.S.-based population with a
reference range upper bound of 3.37 mIU/L for the first trimester
(and 3.35 mIU/L for the second trimester). Therefore, these values
were used to compare to BBDR output TSH values in the first
trimester (or second trimester in cases of gestational weeks 15 and
16) to determine the presence of hypothyroidism.
---------------------------------------------------------------------------

    TSH increases in response to decreases in T4 have been captured in
numerous studies that document the relationship between these hormones
(Blount et al., 2006; Steinmaus et al., 2013, 2016). The EPA designed
the BBDR model to depict this feedback regulation by adjusting a set of
three parameters: The number of sodium-iodide symporter sites, the T4
synthesis rate, and the T3 synthesis rate. The BBDR model allows for
variability in the strength of the TSH feedback by varying these
parameters with a variable called ``pTSH.'' For the MCLG analysis, the
EPA used a pTSH value of 0.398, which is the ratio of a median value
for TSH

from NHANES (non-pregnant women) to the 97.5 percentile value from
NHANES (non-pregnant women). This value represents an assumption that
sensitive individuals with high TSH and average fT4 levels exist, and
this is because the stimulus strength of TSH is proportionally weaker.
The EPA chose to use a low TSH feedback coefficient to ensure the MCLG
is protective of the sensitive population.
    Example output from the BBDR model for gestational week 13 and a
low TSH feedback coefficient is presented in Table III-1.

 Table III-1--Summary of BBDR Model Results for fT4 Levels: Pregnant Women at Gestational Week 13, Assuming Low
                    (75 [micro]g/day) Iodine Intake and with Muted TSH feedback strength \a\
----------------------------------------------------------------------------------------------------------------
                                                 Percentile fT4 (pmol/L) \b\ (% decrease from 0 dose)
  Perchlorate dose  ([mu]g/kg/day)   ---------------------------------------------------------------------------
                                            2.5th               5th                10th               50th
----------------------------------------------------------------------------------------------------------------
0...................................               5.57               6.09               6.70               8.84
1...................................      5.50 (-1.26%)      6.02 (-1.15%)      6.63 (-1.04%)      8.77 (-0.79%)
2...................................      5.43 (-2.45%)      5.96 (-2.24%)      6.56 (-2.04%)      8.71 (-1.54%)
3...................................      5.37 (-3.59%)      5.96 (-3.28%)      6.50 (-2.98%)      8.64 (-2.26%)
4...................................      5.31 (-4.68%)      5.83 (-4.28%)      6.44 (-3.89%)      8.58 (-2.95%)
5...................................      5.25 (-5.73%)      5.77 (-5.23%)      6.38 (-4.76%)      8.52 (-3.60%)
6...................................      5.19 (-6.73%)      5.72 (-6.14%)      6.33 (-5.59%)      8.47 (-4.23%)
7...................................      5.14 (-7.69%)      5.66 (-7.02%)      6.27 (-6.39%)      8.41 (-4.84%)
----------------------------------------------------------------------------------------------------------------
\a\ pTSH = 0.398; see USEPA, (2018b) for additional information on pTSH.
\b\ The 50th percentile is direct output from the BBDR model, and additional percentiles are estimated by
  assuming a normal distribution with a SD of 1.67. All of the examined study data demonstrated a positive skew,
  and overall the lognormal function demonstrated a better fit than a normal distribution. Despite this, the
  available study data only accounted for variation due to gestation week and did not account for variation in
  perchlorate and iodine intake in the measured populations. Because perchlorate and iodine can affect fT4
  levels, and this relationship produced the estimated median BBDR values, the distribution around values
  estimated by the model from perchlorate and iodine intake should account for a small reduction in variation
  due to the effect of perchlorate and iodine intake. Additionally, as iodine has a demonstrated lognormal
  distribution with strong right skew (e.g., Blount et al., 2007) and is predicted to have a stronger effect on
  fT4 than perchlorate (see Section 3). The EPA assumed the error around predicted fT4 would likely be closer to
  normal than lognormal after accounting for perchlorate and iodine intake.

    When modeling changes in fT4, the baseline level of fT4 affects the
magnitude of changes seen as a result of perchlorate exposure.
Therefore, to predict the impact of perchlorate exposure on the
population distribution of fT4 for the identified sensitive population,
the EPA estimated a distribution for fT4 plasma concentrations around
the median modeled values based on fT4 data from studies that were used
to calibrate the BBDR model (C. Li et al., 2014; M[auml]nnist[ouml] et
al., 2011; Zhang et al., 2016). The EPA assumed the variation around
predicted fT4 concentrations for women with low fT4 of childbearing age
would likely be close to normal after accounting for perchlorate and
iodine intake, and thus estimated a combined standard deviation (SD)
using the distributional information from each of the studies (C. Li et
al., 2014; M[auml]nnist[ouml] et al., 2011; Zhang et al., 2016). The
EPA then used the estimated combined SD to predict a distribution of
fT4 around the median fT4 estimated by the BBDR model. To protect the
most sensitive population from adverse effects, the EPA chose to use
the 10th percentile from this distribution of baseline fT4 to conduct
its analyses to account for variability in thyroid hormones in the
population.\5\
---------------------------------------------------------------------------

    \5\ For a discussion on the details of the BBDR model, including
uncertainties associated with the model the reader is directed to
section 3.5 of the MCLG Approaches Report.
---------------------------------------------------------------------------

G. Epidemiological Literature

    The SAB recommended that the EPA integrate BBDR model results with
data on neurodevelopmental outcomes from epidemiological studies. There
is substantial epidemiological evidence that early pregnancy
hypothyroxinemia is a risk factor for a variety of adverse
neurodevelopmental outcomes, including those related to both cognition
and behavior (Costeira et al., 2011; Finken, van Eijsden, Loomans,
Vrijkotte, & Rotteveel, 2013; Ghassabian et al., 2014; Gyllenberg et
al., 2016; Henrichs et al., 2010; J[uacute]lvez et al., 2013; Kooistra,
Crawford, van Baar, Brouwers, & Pop, 2006; Korevaar et al., 2016; Y. Li
et al., 2010; Oostenbroek et al., 2017; P[auml]kkil[auml] et al., 2015;
Pop et al., 2003, 1999; Roman et al., 2013; van Mil et al., 2012).
These individual studies showing that maternal hypothyroxinemia is
associated with offspring neurodevelopment are also supported by three
meta-analyses (including one full systematic review), all of which
conclude maternal hypothyroxinemia is associated with increased risk of
cognitive delay, intellectual impairment, or lower scores on
performance tests when considering the entire body of evidence on this
topic (Fan & Wu, 2016; Thompson et al., 2018; Wang et al., 2016).
Additionally, the American Thyroid Association concludes that
``overall, available evidence appears to show an association between
hypothyroxinemia and cognitive development of the offspring''
(Alexander et al., 2017, p. 337).
    The EPA did not conduct a full systematic review and weight of
evidence evaluation between maternal thyroid hormones and
neurodevelopmental outcomes given: (1) The body of scientific
literature regarding this association, and (2) the SAB recommendation
that the EPA ``consider available data on potential adverse health
effects (neurodevelopmental outcomes) due to thyroid hormone level
perturbations regardless of the cause of those perturbations'' (p. 25).
Instead, the EPA conducted a ``methodologic approach to reviewing the
literature'' to evaluate the body of literature on this topic. This
approach assisted in extrapolating the relationship modeled by the BBDR
model to neurodevelopmental outcomes by concentrating on studies that
allowed for evaluation of incremental changes in fT4 as they relate to
incremental changes in neurodevelopmental outcomes. More specifically,
the EPA only used studies that had sufficient data to show a
quantitative relationship between maternal fT4 and a neurodevelopmental
outcome. The EPA acknowledges that by not giving any weight to the
studies that did not show

a quantitative relationship between fT4 and neurodevelopmental
outcomes, the Agency may be overestimating the dose of perchlorate that
may be associated with adverse neurodevelopmental outcomes. This is a
health protective decision that adds to the margin of safety.
    Ultimately, the EPA developed a dose-response function that
estimates incremental changes in a neurodevelopmental endpoint based on
a given change in thyroid hormone concentration (fT4), which could be
linked to a given dose of perchlorate using the BBDR model.
    The specifics of this ``methodologic approach to reviewing the
literature'' follow. First, the EPA identified and screened the
available 71 epidemiological studies, which potentially pertained to
altered maternal thyroid hormone levels and offspring neurodevelopment
to identify candidates based on the following criteria:
     Compatible with the sensitive life stages identified by
the NRC and SAB;
     Continuous measure of thyroid hormone values (versus
categorical values);
     Low risk of bias based on analysis using the National
Toxicology Program's Office of Health Assessment and Translation (OHAT)
Risk of Bias (ROB) tool score; and
     Access to underlying data.
    Second, using these screening steps, the EPA categorized all 71
studies into three groups. One group consisted of studies that were not
compatible \6\ with extending the BBDR model (40 studies). Another
group consisted of papers that were relevant to the pertinent life
stages but did not have data from which a dose-response analysis could
be conducted (15 studies). This includes studies that compared
differences between groups, for example studies of offspring of mothers
with hypothyroxinemia versus offspring of mothers without
hypothyroxinemia. Consequently, these studies may have provided insight
into the maternal thyroid hormone and offspring neurodevelopment
relationship but did not have enough information to develop a
continuous dose-response function. The last group of papers had data
that may inform a dose-response function (16 studies). This last group
of papers included publications that may have had categorical analyses
but also presented data that assessed fT4 as a continuous variable and
the outcome of interest. In most instances, the continuous fT4 variable
encompassed the full range for fT4 and not just the hypothyroxinemic
range. After excluding one paper due to a high risk of bias (Kastakina
et al., 2006) 15 papers remained that potentially had dose-response
data between a continuous measure of fT4 and various neurodevelopmental
outcomes describing cognition, behavior and other outcomes. The EPA
notes that by selecting the papers that potentially had dose response
data the Agency is deviating from the systematic weight of evidence
review approach to identify those studies that the SAB recommended we
examine to derive the MCLG.
---------------------------------------------------------------------------

    \6\ For example, if the study evaluated the impact of only
neonatal thyroid hormones (i.e., at a potentially sensitive life
stage), it cannot be used because the BBDR model is specific to
early pregnancy. Further, if the study evaluates a population with
an existing disease (i.e., hypothyroidism) that may have a different
response to perchlorate compared to the euthyroid population, it was
not considered compatible with BBDR model results. Additionally, if
the study does not include information on T4 or fT4, it does not
assist in understanding the implications of the BBDR modeling
results. Another reason for exclusion at this stage include that the
study does not have a population with an exposure window (i.e., when
the thyroid hormone measurements are taken) that overlaps with the
outputs for the BBDR model. Specifically, the study should evaluate
thyroid hormone levels in pregnant mothers between conception and
gestational week 16. The neurodevelopmental outcomes could be
measured at any life stage.
---------------------------------------------------------------------------

    Third, from these 15 papers five were selected for dose response
assessment--four related to cognition (Finken et al., 2013; Korevaar et
al., 2016; Pop et al., 2003, 1999) and one related to behavior
(Endendijk, Wijnen, Pop, & van Baar, 2017). The other ten papers were
excluded for a variety of reasons including updated analyses being
presented in a different paper for which dose-response analysis was
being conducted, lack of all the data needed to complete a dose-
response assessment (e.g., dose-response results were presented as
``per standard deviation of fT4'' but the standard deviation needed to
fully interpret the results for a continuous function was not presented
in the paper, statistical methods presented in the paper were
insufficient to allow for the derivation of a concentration response
function), or a lack of a relationship between maternal fT4 as a
continuous variable and the outcome of interest evaluated in the paper.
For example, Noten et al., (2015) found a relationship between maternal
hypothyroxinemia and offspring arithmetic test performance. However,
maternal fT4 as a continuous variable across the entire fT4 range was
not associated with arithmetic test performance. Given this null
finding, as well as the lack of published literature evaluating
maternal fT4 as a continuous variable and arithmetic test performance,
it would be difficult for the Agency to justify setting an MCLG based
on changes in this endpoint.
    As laid out for the peer reviewers, for each study that met the
criteria identified above for dose-response modeling, a relationship
between maternal thyroid hormone levels (specifically fT4) and
offspring neurodevelopment was derived (see USEPA, 2018b). These
relationships were either presented in the original published paper or
derived by the EPA through either the digitization of figures or
through re-analysis of data provided by the study authors. The EPA used
the upper effect estimate (the upper bound of the 95th percent
confidence interval) from each study to assure consideration of the
populations likely to be at greater risk from the dose of perchlorate
associated with a given change in fT4.
    Table III-2 provides a summary of the changes in fT4 predicted to
produce a 1, 2, and 3 percent decrease in any given neurodevelopmental
effect and corresponding perchlorate doses. The choice of 1, 2, and 3%
is based on the analyses for IQ, Mental Development Index (MDI), and
Psychomotor Development Index (PDI). Specifically, a 1%, 2%, or 3%
change from the standardized mean for each test (i.e., 100 points)
equates to a 1, 2, or 3 point change, respectively. The analyses for
anxiety/depression score and SD of reaction time are based on a 1%, 2%,
or 3% change from the study mean of each measure, which for anxiety/
depression is 0.01, 0.02, or 0.03 points, respectively, and for
reaction time is 2.7, 5.4, and 8.1 milliseconds (study mean SD of
reaction time = 270 ms), respectively (Endendijk et al., 2017; Finken
et al., 2013).
    These results provide the potential impacts of perchlorate on
maternal fT4 (as predicted by the BBDR model) and subsequent
neurodevelopmental impacts (derived from the epidemiologic literature
\7\).
---------------------------------------------------------------------------

    \7\ For a more complete description of all the studies evaluated
the reader is directed to Sections 5 and 6 of the MCLG Approaches
Report. For a discussion on the uncertainties related to the
approach the reader is directed specifically to section 6.5.

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

[GRAPHIC] [TIFF OMITTED] TP26JN19.009

[GRAPHIC] [TIFF OMITTED] TP26JN19.010

H. Identifying a Point of Departure for Developing the MCLG

    From the seven analyses presented in Table III-2 above, the EPA
chose to use its independent analysis of the Korevaar et al., (2016)
data (comprising of 3,600 useable mother/child data pairs) as the basis
for calculating the point of departure (POD) for the MCLG. There are
three reasons for this selection: (1) There is sufficient quantitative
data to derive a health impact function for the sensitive population of
interest; (2) the analysis adjusts for an appropriate set of
confounders, and (3) the neurodevelopmental endpoint--intelligence
quotient (IQ)--is more straightforward to interpret because there is
more national and cross-national data available (more on the selection
of this endpoint below). The other studies presented in Table III-2 do
not provide one or more of these features (USEPA, 2018b).
    The five identified papers evaluated a variety of endpoints with
Korevaar et al., (2016) evaluating IQ, Pop, Kuijpens, et al., (1999)
and Pop, Brouwers, et al., (2003) using the Bayley Scale to evaluate
PDI and MDI, Finken, van Eijsden, Loomans, Vrijkotte, and Rotteveel
(2013) evaluating the SD of reaction time, and Endendijk, Wijnen, Pop,
and van Baar (2017) evaluating anxiety/depression scores using the
Child Behavioral Check List (CBCL). The SD of reaction time from Finken
et al., (2013) was not well-received by the peer reviewers (External
Peer Review for U.S. EPA, 2018) because it is difficult to ascertain
the true implications of a change in the SD of reaction time. The
Endendijk et al., (2017) study was identified after the peer review so
no feedback was given on the appropriateness of the endpoint; however,
the anxiety/depression raw score is not an intuitively interpretable
endpoint. Further, neither the Endendijk et al., (2017) nor the Finken
et al., (2013) analyses had functions for the sensitive life stage
(i.e., their analyses were based on the full range of fT4 levels and
did not concentrate on the impacts of low-end fT4 levels). For these
reasons, the Endendijk et al., (2017) and Finken et al., (2013) papers
were not selected for further evaluation.
    The Korevaar et al., (2016) original and independent analyses are
preferable compared to the Pop, Kuijpens, et al., (1999) and Pop,
Brouwers, et al., (2003) studies because neither function derived from
the Pop et al., studies was adjusted for confounders. Additionally,
both Pop et al., papers have an N <50 compared to the Korevaar et al.,
analyses, which have an N of greater than 3,600.\8\
---------------------------------------------------------------------------

    \8\ The original Korevaar et al. (2016) analysis included 3,839
mother/child pairs. The EPA reanalysis of the Korevaar et al. (2016)
data had a slightly lower N of 3,609 due to the exclusion of
subjects with imputed values for maternal fT4.
---------------------------------------------------------------------------

    Although the original Korevaar et al., (2016) analysis was the most
rigorous analysis available in the literature to date, the Korevaar et
al., (2016) EPA reanalysis was chosen over the original analysis
because it included modifications to the analysis at the suggestion of
the peer review panel. The

revised analysis controls for a more parsimonious set of confounders
(e.g., previously included variables such as infant gender, maternal
parity, birthweight, mother's body mass index (BMI), and gestational
age at blood draw that are not related to both the exposure and the
outcome were excluded), thus decreasing the chances of overfitting the
estimation of the association between maternal fT4 and child IQ. The
EPA was prompted to revisit the original Korevaar et al., (2016) model
because of the feedback received during the peer review of the MCLG
Approaches Report. Specifically, a member of the peer-review panel
expressed the following suggestion:
    Korevaar et al., [2016] controlled for instrumental variables (e.g.
gestational week at fT4 measurement) as well as variables that are
consequences of altered fT4 (e.g. maternal BMI), which may have biased
estimates. This study also assumed a log-linear relation between fT4
and the outcome but it is unclear whether the data fit this functional
form better than a linear form. Reanalysis of the data performed by EPA
should not include the variables noted above, which may have driven
measures of association towards the null, and should investigate the
most appropriate functional form to inform decisions about
transformation of fT4 values (External Peer Reviewers for U.S. EPA,
2018, pp. 61-62).
    The EPA responded to this suggestion by developing a causal model
for the effect of maternal fT4 on child IQ to identify the minimum set
of confounding variables, testing the proper functional form of the
relationship between maternal fT4 and child IQ in the Korevaar et al.,
(2016) data, and making decisions about data quality and influential
data points in the analysis. That is, the EPA determined that there
were values of the independent variable of interest, fT4, in the
original analysis that were imputed using multiple imputations. This
could have impacted the effect estimate of the independent variable of
interest with data that were not directly measured. The EPA reanalysis
excludes these non-measured values. Subsequently, the EPA selected the
Korevaar et al., (2016) reanalysis as the most appropriate function
from which to assess the relationship between fT4 and IQ.\9\
---------------------------------------------------------------------------

    \9\ A more complete description of the EPA independent analysis
of the Korevaar et al. (2016) data can be found in Section 6.3.2 of
the MCLG Approaches Report.
---------------------------------------------------------------------------

    As indicated above, the EPA has utilized a health protective
approach to this analysis consistent with the SDWA definition of the
MCLG. The peer reviewers commented that this approach was fit-for-
purpose. In particular, the Agency assumed it could estimate risk
reductions based on evidence of a quantifiable relationship between
thyroid hormone changes and neurodevelopmental outcomes. The existence
of a quantifiable relationship between thyroid hormone changes and
neurodevelopmental outcomes has strong support from the literature on
the subject; however, not every study identified an association between
maternal fT4 and the specified outcome of interest, and the state of
the science on this relationship is constantly evolving. As explained
earlier, the results of the EPA's dose-response literature review
identified 31 studies that evaluated the association between maternal
thyroid hormone levels and offspring neurodevelopment, with
neurodevelopment defined using a variety of endpoints related to
cognition, behavior, and other outcomes such as autism. Among these
studies, only 16 were deemed to potentially possess information that
could inform a dose-response relationship. The other 15 only presented
data on categorical analyses assessing the impact of maternal
hypothyroxinemia on the neurodevelopmental outcomes of interest.
Therefore, because the data presented was only a comparison of two
groups, there was not information that could be used to inform a dose-
response function.
    Of the 16 studies that potentially had data to inform a dose-
response function, 10 evaluated cognition using a variety of tests
including various IQ tests (three papers; Ghassabian et al., 2014;
Korevaar et al., 2016; Moleti et al., 2016), Bayley Scales of Infant
Development (two papers; Pop et al., 1999; Pop et al., 2003), and other
validated tests associated with child cognition such as expressive
language delay or test performance (five papers; Finken et al., 2013;
Henrichs et al., 2010; Kastakina et al., 2006; Noten et al., 2015; Oken
et al., 2009). Six of these papers found a statistically significant
relationship between maternal fT4, as a continuous variable, and
offspring cognitive outcome (Korevaar et al., 2016; Pop et al., 1999;
Pop et al., 2003; Finken et al., 2013; Henrichs et al., 2010, Kastakina
et al., 2006). However, there were studies where maternal fT4 as a
continuous variable was not significantly associated with the outcome
of interest. For example, in Ghassabian et al., (2014) the authors
found maternal hypothyroxinemia to be associated with an average of a
4.3-point reduction in IQ in their offspring compared to offspring of
non-hypothyroxinemic mothers. Nevertheless, when assessing the
relationship between the continuous measure of maternal fT4 as a
continuous variable (across the entire range of fT4 levels) and child
IQ, the authors did not find a significant relationship. Additionally,
Moleti et al., (2016) found the relationship between maternal fT4 and
child IQ to be consistently inversely associated with IQ scores, but
their assessment failed to reach statistical significance. This study
included fewer than 60 study participants and was considered by the
authors to be a pilot assessment.
    In addition to the cognitive effects assessed and modeled, the EPA
identified four papers that assessed maternal fT4 status and behavioral
outcomes (Endendijk et al., 2017; Ghassabian et al., 2011; Modesto et
al., 2015; Oostenbroek et al., 2017), one paper that assessed maternal
fT4 status and autism (Roman et al., 2013) and one paper that evaluated
odds of a schizophrenia diagnosis as associated with maternal thyroid
hormone status (Gyllenberg et al., 2016). From this group of papers,
the majority of papers found an association either between maternal
hypothyroxinemia or maternal fT4 as a continuous variable and the
outcome of interest (Endendijk et al., 2017; Modesto et al., 2015;
Oostenbroek et al., 2017; Roman et al., 2013; Gyllenberg et al., 2016).
However, this was not always the case as exemplified by Ghassabian et
al., (2011) and Gyllenberg et al., (2016). Although Endendijk et al.,
(2017) found maternal fT4 to have a significant adverse impact on
anxiety/depression using the Child Behavioral Check List (CBCL),
Ghassabian et al., (2011) did not find any association between maternal
thyroid hormone status and offspring score on various components of the
CBCL. Additionally, Gyllenberg et al., (2016) found maternal
hypothyroxinemia during early to mid-gestation was associated with 70%
increased odds of schizophrenia diagnosis in offspring of
hypothyroxinemic mothers compared to the offspring of non-
hypothyroxinemic mothers. Gyllenberg et al., (2016) also found an
association with odds of schizophrenia diagnosis using conditional
logistic regression when assessing fT4 as a continuous variable across
the entire fT4 range (i.e., not just the hypothyroxinemic range);
however, this relationship was attenuated after controlling for
smoking.
    Not every paper the EPA located in its literature review found a
statistically

significant association between maternal fT4 as a continuous variable
(i.e., the initially identified 16 studies identified as potentially
useful to inform a dose-response function) and the neurodevelopmental
outcome of interest. However, many studies located in the EPA
literature review, several meta-analyses (Fan & Wu, 2016; Thompson et
al., 2018 and Wang et al., 2016), the American Thyroid Association
(Alexander et al., 2017) and the U.S. EPA's SAB (2013) have concluded
there is a relationship between maternal hypothyroxinemia and various
neurodevelopmental outcomes. The relationship between maternal fT4
levels and neurodevelopmental outcomes appears strongest in the
hypothyroxinemic range, and when looking at the entire range of fT4 as
a continuous variable (as opposed to a categorical cut off), the
significant relationship between the two variables may dissipate.
Therefore, the EPA has concentrated on the neurodevelopmental impacts
of changes in fT4 in the lower range of fT4 from the Korevaar et al.,
(2016) data. In an attempt to minimize uncertainty, the EPA reanalyzed
the data collected by Korevaar et al., (2016) using a spline function
that estimates a coefficient specifically for the low range of the fT4
data.
    There are a variety of neurodevelopmental endpoints used to examine
behavior and cognition in children (e.g., intelligence quotient (IQ),
motor skills, vocabulary and language development, stimulus
responsiveness, etc.). The EPA selected IQ decrements because this was
the endpoint evaluated in the Korevaar et al., (2016) study. The EPA
determined that the Korevaar study was the most rigorous analysis that
examined the relationship between decreased thyroid hormones and
neurodevelopmental effects. As such, in the derivation of the MCLG, IQ
is a surrogate for a suite of potential neurodevelopmental effects that
might occur to the offspring of hypothyroxinemic and iodine deficient
mothers.
    There are several different tests that are widely used to measure
IQ in children, including the Stanford-Binet and the Wechsler
Intelligence Scale for Children (WISC) (Sternberg et al., 2001). Each
of these tests is intended to assess a child's global functioning and
uses a numerical IQ point scale (Beres et al., 2000). IQ scores are
standardized by age and sex group with a mean score of 100 points and a
standard deviation of 15 (Beres et al., 2000). Although the specific
tasks differ by test, all IQ tests contain a number of tasks to assess
diverse skills (Sternberg et al., 2001). For example, the WISC test
evaluates full-scale IQ using a combination of verbal and performance
scales (verbal IQ and performance IQ may also be assessed separately)
(Beres et al., 2000). The verbal scale includes tasks such as
arithmetic, vocabulary, and comprehension, while the performance scale
includes tasks such as picture completion, block design, and object
assembly (Beres et al., 2000). The WISC was standardized using a sample
of 2200 U.S. children aged 6 to 16 years old (Seashore et al., 1950).
It has been well validated and has demonstrated high reliability, with
a reliability coefficient of 0.96 observed across age groups (Beres et
al., 2000).
    Associations have been found between IQ scores and both educational
achievement and attainment, though observed correlations vary widely.
In a review of the literature, Sternberg et al., (2001) suggest that IQ
scores explain approximately 25% of the variance in academic
achievement. Evidence also suggests that IQ is linked to career
outcomes and job performance, with observed correlations ranging from
approximately 0.2 to 0.6 (Sternberg et al., 2001). Research suggests
that children's rearing environment, including parental education,
while growing up may increase IQ scores in adolescence by several
points (e.g., Kendler et al., 2015).
    IQ scores have been used to help diagnose disorders such as
intellectual disability and to identify children for placement into
specialized learning programs (Beres et al., 2000). For example, in the
Diagnostic and Statistical Manual of Mental Disorders, fifth edition
(DSM-V) IQ scores are used in an individual's comprehensive assessment
to determine intellectual disability, which pairs standardized testing
of intelligence with a clinical assessment of adaptive functioning.
Intellectual disability is considered for individuals with an IQ score
of about 70 or below (American Psychiatric Association, 2013).
    The EPA uses a variety of science policy approaches to select
points of departure for developing regulatory values. For instance, in
noncancer risk assessment the EPA often uses a percentage change in
value. When assessing toxicological data, a 10 percent extra risk (for
discrete data), or a 1 standard deviation (i.e., 15 IQ points) change
from the mean (for continuous data) is often used (USEPA, 2012). A
smaller response to inform a POD has been applied when using
epidemiological literature because there is an inherently more direct
relationship between the study results and the exposure context and
health endpoint. Given the difficulty in identifying a response below
which no adverse impact occurs when considering a continuous outcome in
the human population, the EPA looked to its Benchmark Dose Guidance
(2012) for insight regarding a starting point. Specifically, ``[a] BMR
of 1% has typically been used for quantal human data from epidemiology
studies'' (p. 21, USEPA, 2012).
    For the specific context of setting an MCLG for perchlorate, the
EPA made a policy decision to evaluate the level of perchlorate in
water associated with a 1 percent decrease, a 2 percent decrease, and a
3 percent decrease in the mean population IQ (i.e., 1, 2 and 3 IQ
points). The EPA selected IQ as a surrogate for neurodevelopmental
effects based upon its evaluation of the epidemiologic literature
describe above. The need to utilize the best available peer reviewed
data to inform scientific assumptions and policy choices to meet the
statutory requirements associated with developing an MCLG under the
SDWA highlights the challenges associated with regulating chemicals for
which potential effects are indirect, and scientific data do not
address all uncertainties. The Agency must make a policy decision
informed by science, consistent with statutory requirements even in
situations where the data do not provide clear choices. To develop the
proposed MCLG for perchlorate, the EPA made a policy decision to use a
2 IQ point decrement in the population-distribution of IQ for the
sensitive population. By selecting this approach, the EPA is not
establishing a precedent for future Agency actions on other
contaminants for which there is concern about potential thyroid
effects, either under the SDWA or other statutory frameworks.
    Applying these response rates to the results from the reanalysis of
Korevaar et al., (2016), results in a POD dose of 3.1 [micro]g/kg/day
for a 1 point decrease in the sensitive population's IQ, a POD dose of
6.7 [micro]g/kg/day for a 2 point decrease in the sensitive
population's IQ, and a POD dose of 10.8 [micro]g/kg/day for a 3 point
decrease in the sensitive population's IQ. These PODs associated with a
1, 2, or 3 point decrease from the standardized mean IQ are calculated
for the most sensitive population. Specifically, the POD is designed to
provide an adequate margin of safety for the fetuses of mothers with
fT4 at the 10th percentile of a population with iodine intake of 75
[micro]g/day and a TSH feedback loop that is less than 60% as effective
as individuals with median

TSH feedback loop efficacy. That is, the analysis is designed to
protect the population of fetuses of mothers with suboptimal thyroid
functioning. For these reasons, and for the methodological reasons
described previously, the EPA believes that the selection of these
parameters and this point of departure assures no known or anticipated
adverse effects on the health of the most sensitive population and
allows for an adequate margin of safety.

I. Translate PODs to RfDs

    When deriving an RfD the EPA evaluates whether to apply
uncertainty/variability factors to account for heterogeneity of effect
in the target population and data gaps (USEPA, 2002). As presented in A
Review of the RfD & RfC Processes (USEPA, 2002) the EPA considers the
following uncertainty factors: Inter-individual variability,
interspecies uncertainty, extrapolating from subchronic to chronic
exposure, extrapolating from a lowest-observed adverse effect level
(LOAEL) rather than from a no-observed-adverse-effect-level (NOAEL),
and an incomplete database. The factors are intended to account for:
(1) Variation in susceptibility among the members of the human
population (i.e., inter-individual or intraspecies variability); (2)
uncertainty in extrapolating animal data to humans (i.e., interspecies
uncertainty); (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 rather than from a NOAEL; and (5) uncertainty associated with
extrapolation when the database is incomplete. (U.S. EPA, 2011b) The
EPA has considered each of these factors in deriving an RfD to inform
an MCLG for perchlorate.
    The EPA considered variation and uncertainty in the relationship
between exposure and response among the members of the human population
(i.e., uncertainty factor (UF) for within-human variability/inter-
individual variability, UFH). For this analysis a UF of 3 is
used. The approach taken to derive the RfD attempts to address
variability between the general population and the sensitive
population. Specifically, the EPA was able to modify the strength of
the TSH feedback loop and iodine intake levels in the BBDR model and
concentrate on the dose-response relationship between lower level (as
opposed to median level) fT4 and neurodevelopmental outcomes. However,
there is still uncertainty in the relationship between perchlorate
exposure and subsequent neurodevelopmental outcomes.\10\ There are very
few toxicokinetic calibration data available for the perchlorate to
thyroid hormone relationship described in the BBDR model. On the
toxicodynamic side of the BBDR model, aspects such as competitive
inhibition at the NIS, depletion of iodide stores under different
iodine intake levels and physiological states, and the ability of the
TSH feedback loop to compensate for perturbations in thyroid function
each have their own uncertain features. There are also uncertainties
linking maternal fT4 levels to offspring IQ. These uncertainties
include the population for which dose-response information is available
(i.e., no study is U.S. based), a lack of study information on the
iodine intake status for the population for which the dose-response
information is available, uncertainties around the methods used to
assess maternal fT4 measurement during pregnancy, and uncertainties
related to the true distribution of fT4 for a given iodine intake.
---------------------------------------------------------------------------

    \10\ For a more complete discussion on the uncertainties in the
analysis the reader is directed to Sections 3.5 and 6.5 of the MCLG
Approaches Report.
---------------------------------------------------------------------------

    Further, as discussed in section III.C. of this preamble the EPA
believes that protecting the fetus of a hypothyroxinemic woman will
protect other identified sensitive life stages. However, there is some
uncertainty due to the lack of information linking incremental changes
in infant thyroid hormone levels to adverse neuorodevelopmental
outcomes. In addition, this analysis is assuming that protecting a
first trimester fetus from alterations in maternal fT4 will protect the
fetus throughout pregnancy. This is based on epidemiologic evidence
that shows the relationship between first trimester maternal fT4 and
neurodevelopmental outcomes. This is potentially because before mid-
gestation, the mother is the only source of thyroid hormone for the
fetus (Morreale de Escobar et al., 2004). Therefore, when evaluating
maternal fT4 as associated with neurodevelopmental outcomes it is
critical to understand the first-trimester levels. Later in gestation,
when the fetal thyroid begins secreting thyroid hormones, maternal fT4
may no longer be a good surrogate for the thyroid hormone levels
available to the fetus. Given that the fetal thyroid has had little
time to develop, its iodine storage is much less than that of an adult,
hence there may be more sensitivity to short-term fluctuations in
iodine availability and uptake that may have little impact on maternal
levels. Therefore, there is some uncertainty about the impact
perchlorate may have on the fetal thyroid gland, and subsequent
neurodevelopmental impacts, in later trimesters of pregnancy. The
immature fetal HPT axis has very limited capacity to increase output of
thyroid hormones (Savin, Cveji[cacute], Nedi[cacute], &
Radosavljevi[cacute], 2003; van Den Hove, Beckers, Devlieger, De
Zegher, & De Nayer, 1999), so the fetal HPT may not be able to adjust
output in the face of reduced maternal fT4 supply and perchlorate
exposure. Therefore, as described above, the EPA selected an
intraspecies UF of 3 to account for the uncertainties in modeling the
impacts of perchlorate ingestion on the thyroid hormone levels for
pregnant mothers with low iodide intake, and the uncertainties in
predicting the neurodevelopmental effects of these thyroid hormone
changes on their children.
    The EPA considered but did not derive a Data-Dependent
Extrapolation Factor (DDEF) for this analysis. As described above, the
UFs are applied based on the uncertainties in the perchlorate to
thyroid hormone and thyroid hormone to neurodevelopment
relationship.\11\ As noted above, the Agency has opted to apply a UF of
3 to the POD, which adds an adequate margin of safety to the MCLG
derivation. Section <IP_ADDRESS> (p. 4-42) of A Review of the RfD & RfC
Processes recommends reducing the intraspecies UF from a default of 10
``only if data are sufficiently representative of the exposure/dose-
response data for the most susceptible subpopulation(s)'' (p. xviii,
USEPA, 2002). The EPA selected a UF of 3 instead of the full 10 because
the modeled groups within the population that are identified as likely
to be at greater risk to perchlorate in drinking water (i.e., the fetus
of the iodide deficient pregnant mother) and has selected model
parameters to account for the most sensitive individuals in that group
(i.e., muted TSH feedback, low fT4 values, low-iodine intake).
---------------------------------------------------------------------------

    \11\ As explained in U.S. EPA, 2014 ``UFs incorporate both
extrapolation components that address variability (heterogeneity
between species or within a population) and components that address
uncertainty (i.e., lack of knowledge) . . . whereas DDEFs focus on
variability'' (p. 7, US EPA, 2014).
---------------------------------------------------------------------------

    Below we list the other uncertainty factors added and the
justification.
     Uncertainty in extrapolating animal data to humans (i.e.,
interspecies uncertainty) (uncertainty factor, animal-to-human,
UFA). For this analysis an UF of 1 is used because this
factor is not applicable since animal studies were

not used to develop the BBDR model nor were they used to relate
alterations in maternal fT4 to IQ.
     Uncertainty in extrapolating data obtained in a study with
less-than-lifetime exposure to lifetime exposure (i.e., extrapolating
from subchronic to chronic exposure, UFS). An uncertainty
factor of 1 is used. Extrapolating from subchronic to chronic exposures
did not occur as the BBDR model was designed to assess long-term
steady-state conditions in the non-pregnant woman and week-to-week
variation in pregnancy, rather than short-term (hour-to-hour or day-to-
day) fluctuations.
     Uncertainty in extrapolating from a LOAEL rather than from
a NOAEL (uncertainty factor, LOAEL-to-NOAEL, UFL). A more
sophisticated BBDR modeling approach, coupled with extrapolation to
changes in IQ using linear regression, was used to determine a POD that
would not be expected to represent an adverse effect. Subsequently an
uncertainty factor of 1 is used. LOAELs and NOAELs were not identified
or used in this approach.
     Uncertainty factor for database deficiency to address the
potential for deriving an inadequately protective RfD in the instance
where the available database provides an incomplete characterization of
the chemical's toxicity (database deficiency, UFD; USEPA,
2002). An uncertainty factor of 1 is used as ``[t]he mode of action of
perchlorate toxicity is well understood'' (SAB for the U.S. EPA, 2013,
p. 2).
     The product of all the uncertainty factors
(UFH) is 3 (3 x 1 x 1 x 1 x 1).
    Below we generate RfD's for each of the points of departure.
    Using the POD of 6.7 [mu]g/kg/day based on a 2 percent decrease in
the population standardized mean IQ from the EPA's independent analysis
of the Korevaar et al., (2016) data, the EPA can derive a RfD by
incorporating the UFH, which results in the following:
[GRAPHIC] [TIFF OMITTED] TP26JN19.011

    Using an alternative POD of 3.1 [mu]g/kg/day based on a 1 percent
decrease in the population standardized mean IQ from the EPA's
independent analysis of the Korevaar et al., (2016) data, the EPA can
derive an RfD by incorporating the UFH. This results in the
following:
[GRAPHIC] [TIFF OMITTED] TP26JN19.012

    Using an alternative POD of 10.8 [mu]g/kg/day based on a 3 percent
decrease in the population standardized mean IQ from the EPA's
independent analysis of the Korevaar et al., (2016) data, the EPA can
derive an RfD by incorporating the UFH. This results in the
following:
[GRAPHIC] [TIFF OMITTED] TP26JN19.013

J. Translate RfD Into an MCLG

    To translate the RfD ([mu]g/kg/day) to a concentration in drinking
water ([mu]g/L), the EPA used the following equation:
[GRAPHIC] [TIFF OMITTED] TP26JN19.014

Where:

W = drinking water concentration of perchlorate in micrograms per
liter ([mu]g/L);
RfD = reference dose (1.03 [mu]g/kg/day for a 1 percent decrease in
IQ, 2.23 [mu]g/kg/day for a 2 percent decrease in IQ, or 3.6 [mu]g/
kg/day for a 3 percent decrease in IQ);
DWI = bodyweight-adjusted drinking water ingestion rate (L/kg/day);
and
RSCw = relative source contribution of drinking water to
overall perchlorate exposure.

    To calculate the MCLGs, the EPA selected the 90th percentile body-
weight adjusted drinking water ingestion rate specific to women of
childbearing age (i.e., non-pregnant, non-lactating, 15-44 years of age
(0.032 L/kg/day). This decision is consistent with the analysis used in
deriving an RSC, which was performed using food consumption information
for a population of women of childbearing age from NHANES. The 90th
percentile is chosen to account for variability in drinking water
ingestion rates, but also adds another layer of health protection for
90% of women (Table III-3).
    The EPA did not use water intake data for pregnant women because
the sample sizes were too small to be statistically stable. The use of
the drinking water intake for 15-44 year old women is consistent with
the analysis used in deriving an RSCw (described below),
which was performed using food consumption information for a population
of women of childbearing age from NHANES. The EPA acknowledges there is
a difference in the age range defining women of childbearing age used
to develop the drinking water ingestion rate and that used to develop
the RSC (20-44 years of age). The age range used to develop the

RSC was based on the range of ages used to define women of childbearing
age in developing the BBDR model. However, the EPA's Exposure Factors
Handbook (USEPA, 2011c) identifies drinking water ingestion rates for
women 15-44 years of age as corresponding to women of childbearing age.
    The age range used for women of childbearing age in the BBDR model
fits within the age range used to develop the ingestion rates provided
in the Exposure Factors Handbook. Thus, the Agency believes the
difference in the age ranges will have minimal impact on the resulting
MCLG analysis.

 Table III-3--Consumers-Only Estimated Direct and Indirect Community Water Ingestion Rates From Kahn and Stralka
                                                     (2008)
                                                   [L/kg/day]
----------------------------------------------------------------------------------------------------------------
                                                                                90th            95th
          Female population categories              Sample size        Mean         Percentile      Percentile
----------------------------------------------------------------------------------------------------------------
Pregnant........................................              65       \a\ 0.014       \a\ 0.033       \a\ 0.043
Lactating.......................................              33       \a\ 0.026       \a\ 0.054       \a\ 0.055
Non-pregnant, non-lactating, 15 to 44 years of             2,028           0.015           0.032           0.038
 age............................................
----------------------------------------------------------------------------------------------------------------
\a\ The sample size does not meet minimum reporting requirements to make statistically reliable estimates as
  described in the Third Report on Nutrition Monitoring in the United States, 1994-1996 (FASEB/LSRO, 1995).

    Individuals are exposed to perchlorate through ingestion of both
food and drinking water (ATSDR 2008, Huber et al., 2011). In
calculating the MCLGs, the EPA applies a relative source contribution
(RSC) to the RfD to account for the percentage of the RfD remaining for
drinking water after other sources of exposure to perchlorate have been
considered. Thus, the RSC for drinking water is based on the following
equation where ``Food'' is the perchlorate dose from food ingestion:
[GRAPHIC] [TIFF OMITTED] TP26JN19.015

    To estimate the dose of perchlorate for women of childbearing age
coming from food, the EPA implemented a data integration methodology
that combined demographic variables, food consumption estimates, and
perchlorate contamination estimates in food from multiple sources
(USEPA, 2019c). These sources include:
     The NHANES data available from the Centers for Disease
Control and Prevention's (CDC) National Center for Health Statistics
(NCHS) including the What We Eat in America (WWEIA) 24-hour food diary
data (CDC & NCHS, 2007, 2009, 2011); and
     The Food and Drug Administration's (FDA's) Total Diet
Study (TDS) (U.S. Food and Drug Administration (FDA), 2015), which
analyzes contaminants in about 280 kinds of food and beverages commonly
consumed by the U.S. population.
    The NHANES data provided individual food consumption profiles for
female participants age 20-44 (the women of childbearing age range used
for the BBDR model). The EPA matched TDS perchlorate concentrations
with each food consumed by a participant and calculated each
participant's daily perchlorate dose ([mu]g/kg/day) from food using the
participant's body weight. The EPA estimated each participant's
perchlorate dose using both mean and 95th percentile perchlorate
concentrations in food. The details of these assumptions are explained
on page 5-5 of the Technical Support Document: Deriving a Maximum
Contaminant Level Goal for Perchlorate in Drinking Water (USEPA 2019c).
Specifically, the EPA calculated both the mean and the 95th percentile
of the perchlorate levels in each food based on the 20 samples included
in the TDS data. In order to estimate the 95th percentile from the 20
samples, the EPA used the second-highest test result for each food to
represent the 95th percentile concentration. While simple, this method
avoids the need to assume a distributional shape for the samples, and
has been used in recent publications of TDS data for iodine (Carriquiry
et al., 2016). The aforementioned method for identifying the 95th
percentile concentration of perchlorate from food was selected over
other, more ``statistically based'' methods for estimating percentiles
as it avoids the need to assume a distributional shape for the samples.
The EPA determined that it was more reliable to assume the empirically
derived distribution as the basis for selecting the 95th percentile
(i.e., assuming the distribution was equal to the distribution of
samples collected in the TDS), as opposed to forcing a distributional
shape, such as normal or log-normal, onto the data that may not
necessarily be appropriate. With the chosen method, we can at least be
sure that the distributional shape is appropriate for the data at hand,
whereas by choosing the alternative that assumes a distributional
shape, in many instances we would not even be certain of that. The EPA
used these individual bodyweight-adjusted perchlorate doses from food
to calculate distributions of perchlorate dose from food for the
population of women age 20-44.
    Table III-4 presents the mean and selected percentiles of the
distribution of perchlorate dose from food for women ages 20-44, for
both mean and 95th percentile perchlorate concentrations in food based
on the TDS. To calculate the RSC, the EPA selected the 90th percentile
dose of perchlorate from food, assuming a scenario where the food
contained the 95th percentile perchlorate concentration. This
corresponds to a perchlorate dose for food of 0.45 [mu]g/kg/day. The
EPA chose to use the 90th percentile bodyweight-adjusted perchlorate
consumption from food using the 95th percentile TDS results to estimate
the perchlorate RSC from drinking water. The EPA believes this is the
most appropriate value for perchlorate consumption from food to ensure
the protection of potentially highly exposed individuals. Given the
range of perchlorate concentrations in food, and that food is the only
other exposure source being considered in the RSC analysis, the EPA
believes it is sufficiently protective to estimate the MCLG for
drinking water using the 90th percentile bodyweight-adjusted
perchlorate consumption based on the 95th percentile perchlorate food
concentrations in TDS. This assures that highly exposed individuals
from this most sensitive population are considered in the evaluation of
whether perchlorate is found at levels of health concern.

     Table III-4--Perchlorate Dose From Food ([mu]g/kg/day) in U.S. Women Ages 20-44 Using the Mean and 95th
                                           Percentile TDS Results \1\
----------------------------------------------------------------------------------------------------------------
                                                                      Perchlorate dose from food ([mu]g/kg/day)
                                                                   ---------------------------------------------
     Level of bodyweight adjusted perchlorate consumption from                                 Based on 95th
                      population distribution                           Based on mean            percentile
                                                                      concentrations of      concentrations of
                                                                     perchlorate in food    perchlorate in food
----------------------------------------------------------------------------------------------------------------
Mean..............................................................              0.09-0.12              0.23-0.24
50th Percentile...................................................              0.08-0.10              0.17-0.19
90th Percentile...................................................              0.18-0.21                   0.45
99th Percentile...................................................              0.33-0.38              1.16-1.17
----------------------------------------------------------------------------------------------------------------
\1\ Ranges are due to various approaches for handling values level of detection. If no range is presented all
  approaches resulted in the same value.
Bolded value represents the selected value.

    The EPA used the drinking water intake and perchlorate dose from
food to calculate MCLGs for the three RfD values. Table III-5 shows the
RSC values for the three RfD values and the corresponding MCLGs
calculated using the EPA's standard equation.
[GRAPHIC] [TIFF OMITTED] TP26JN19.016

IV. Maximum Contaminant Level Goal and Alternatives

    Section 1412(a)(3) of the SDWA requires the EPA to propose a
maximum contaminant level goal (MCLG) simultaneously with the NPDWR.
The MCLG is defined in Section 1412(b)(4)(A) as ``the level at which no
known or anticipated adverse effects on the health of persons occurs
and which allows an adequate margin of safety.'' The EPA is proposing
an MCLG of 56 [mu]g/L based on the rationale and methology described in
Section III above. The derivation of the proposed MCLG uses a point of
departure based upon a two percent decrease in IQ for offspring of
hypothyroxinemic women of child bearing age have with low iodine
intake. The EPA selected a 2 percent decrease in IQ for the proposed

perchlorate MCLG because this represents a small change in IQ, well
below one standard deviation for the subpopulation of interest.
    As described in Section III, the EPA has selected model parameters
and other factors for the derivation of the MCLG that are health
protective, including the focus on the most sensitive life stage. The
EPA believes that the selection of the combination of protective
parameters and this point of departure assures no known or anticipated
adverse effects on the health of the most sensitive subpopulation and
allows for an adequate margin of safety. The EPA also acknowledges the
uncertainties in the derivation of the proposed (and alternative)
MCLGs. The EPA acknowledges in particular the challenge associated with
selecting the decrement of IQ that represents an adverse effect at the
population level and the uncertainties in predicting the dose of
perchlorate that may result in a particular IQ decrement given the
absence of robust human epidemiological data directly linking
perchlorate exposure to IQ decrements. The Agency seeks comment on the
alternative MCLG values of 18 [mu]g/L and 90 [mu]g/L, which the EPA
derived using the methodology described in Section III based on a one
percent and three percent decrease in IQ, respectively.

V. Maximum Contaminant Level and Alternatives

    Under section 1412(b)(4)(B) of the SDWA, the EPA must establish a
maximum contaminant level (MCL) as close to the MCLG as is feasible.
The EPA evaluated available analytical methods to determine the lowest
concentration at which perchlorate can be measured and evaluated the
treatment technologies for perchlorate that have been examined under
field conditions (USEPA 2018a, 2019b). The EPA determined that setting
an MCL equal to the proposed MCLG of 56 [mu]g/L is feasible given that
the approved analytical method for perchlorate for UCMR 1 has a minimum
reporting level (MRL) of 4 [mu]g/L (USEPA 1999, 2000c) and that
available treatment technologies can treat to concentrations well below
56 [mu]g/L (USEPA, 2018c). Therefore, the EPA is proposing to set the
MCL for perchlorate at 56 [mu]g/L.
    Because the EPA is taking comment on alternative MCLG values of 18
[mu]g/L and 90 [mu]g/L the Agency evaluated the feasibility of setting
an MCL at these levels. The EPA determined that the proposed MCL of 56
[mu]g/L is feasible, therefore a higher MCL alternative such as 90
[mu]g/L is also feasible. The EPA has concluded that analytical methods
are capable of measuring perchlorate at 18 [mu]g/L and that treatment
technologies have been demonstrated to achieve this level under field
conditions (USEPA 2018a, 2019b). Therefore, the EPA is requesting
comment on the feasibility of the proposed MCL of 56 [mu]g/L as well as
the feasibility of the alternative MCLs of 18 [mu]g/L and 90 [mu]g/L.
    As the occurrence analysis in section VI demonstrates, there is
infrequent occurrence of perchlorate at 18 [mu]g/L, 56 [mu]g/L, or 90
[mu]g/L. Therefore, the EPA did not evaluate alternative MCL values
greater than the corresponding MCLG values. The purpose for evaluating
alternative MCL values is to determine whether there is an MCL at which
benefits justify the costs of setting an MCL. Given infrequent
occurrence, the majority of the costs associated with establishing an
NPDWR for perchlorate are for administrative and initial monitoring
activities (see section XI.B), which will not be significantly affected
by MCL values greater than corresponding MCLG values.
    When proposing an MCL, the EPA must publish, and seek public
comment on, the health risk reduction and cost analyses (HRRCA) of each
alternative MCL considered (SDWA Section 1412(b)(3)(C)(i)), including:
The quantifiable and nonquantifiable health risk reduction benefits
attributable to MCL compliance; the quantifiable and nonquantifiable
health risk reduction benefits of reduced exposure to co-occurring
contaminants attributable to MCL compliance; the quantifiable and
nonquantifiable costs of MCL compliance; the incremental costs and
benefits of each alternative MCL; the effects of the contaminant on the
general population and sensitive subpopulations likely to be at greater
risk of exposure; any adverse health risks posed by compliance; and
other factors such as data quality and uncertainty. The EPA provides
this information in section XII in this preamble. The EPA must base its
action on the best available, peer-reviewed science and supporting
studies, taking into consideration the quality of the information and
the uncertainties in the benefit-cost analysis (SDWA Section
1412(b)(3)). The following sections, as well as the health effects
discussion in section III document the science and studies that the EPA
relied upon to develop estimates of benefits and costs and understand
the impact of uncertainty on the Agency's analysis.

VI. Occurrence

    The UCMR 1 is the primary source of occurrence data the EPA relied
on to estimate the number of water systems (and associated population)
expected to be exposed at levels of perchlorate which could potentially
exceed the proposed and alternative MCL levels. Since UCMR 1 data was
first used to inform the Agency actions on the 2008 preliminary
regulatory determination and the 2011 final regulatory determination,
the Agency has modified its analysis of the UCMR 1 data set in response
to concerns raised by stakeholders regarding the data quality and to
represent current conditions at some States that have enacted
perchlorate regulations since the UCMR 1 data was collected. Despite
these updates, the EPA continues to rely on the UCMR 1 data because
they are the best available data collected in accordance with accepted
methods from a census of the large water systems (serving more than
10,000 people) and a statistically representative sample of small water
systems that provides the best available, national assessment of
perchlorate occurrence in drinking water.
    In 1999, the EPA developed the first round of the UCMR program in
accordance with SDWA requirements to provide national occurrence
information on unregulated contaminants (USEPA, 1999, 2000b). The UCMR
1 required sampling from systems in all 50 States, the District of
Columbia, four U.S. territories, and tribal lands in five EPA Regions
including:
     All 3,097 large (serving more than 10,000 people) CWSs and
NTNCWSs, which analyzed either four quarterly samples collected at 3-
month intervals (surface water sources), or two samples collected 5 to
7 months apart (ground water sources); and
     a statistically representative selection of 800 small CWSs
and NTNCWSs, which analyzed either four quarterly samples collected at
3-month intervals (surface water sources) or two samples collected 5 to
7 months apart (ground water sources).
    Water systems submitted UCMR 1 sampling results to the EPA from
2001 until 2005. Water systems were required to analyze samples for 26
contaminants including perchlorate. The EPA established a minimum
reporting level of 4 [mu]g/L for perchlorate in the UCMR.
    The EPA conducted a data quality review of the UCMR 1 data
submitted by systems prior to analyzing the occurrence data for the
2011 perchlorate regulatory determination. The UCMR 1 dataset used by
the EPA included 34,331 samples with 637 measurements of perchlorate
above the minimum reporting level from 3,865 systems.
    In September of 2012, the EPA received a ``Request for Correction''

letter from the United States Chamber of Commerce regarding information
and data (i.e., the occurrence of perchlorate in drinking water) used
by the EPA in its 2011 determination to regulate perchlorate. The U.S.
Chamber of Commerce letter stated that the EPA relied upon: (1) Data
that did not comply with data quality guidelines and (2) data that was
not representative of current conditions.
    In response \12\ to the U.S. Chamber of Commerce, the EPA conducted
a detailed assessment of the source water sample detections and
determined that it was most appropriate to exclude the source water
sample detections from the UCMR 1 perchlorate data set when those
samples had appropriate follow-up entry point samples that were
included in the UCMR 1 perchlorate data set. In contrast, any source
water sample perchlorate detections for which no follow-up entry point
sampling was conducted by PWSs were retained in the UCMR 1 perchlorate
data set. As a result of the assessment, the EPA removed 199 source
water samples (97 detections) that could be paired with a second
follow-up sample located at the entry point to the distribution system.
Following this convention, the resulting UCMR 1 data set contains
34,132 perchlorate samples from 3,865 systems with a total of 540
detections from 149 PWSs.
---------------------------------------------------------------------------

    \12\ See the EPA response letter at https://www.epa.gov/sites/production/files/2017-08/documents/12004-response_0.pdf.
---------------------------------------------------------------------------

    Table VI-1 shows sample distribution by system size category and
measurement status. It also shows the number of entry points and
systems where perchlorate measurements were reported. The entry point
estimates differ from the system estimates because many water systems
have more than one entry point. For example, a ground water system with
two wells that has separate connections to the distribution system has
two entry points.
    In response to the U.S. Chamber of Commerce request, the EPA has
also reassessed the UCMR 1 data in light of the adoption of regulatory
limits in two states. Massachusetts promulgated a drinking water
standard for perchlorate of 2 [mu]g/L in 2006 (MassDEP, 2006), and
California promulgated a drinking water standard of 6 [mu]g/L in 2007
(California Department of Public Health, 2007). Systems in these states
are now required to keep perchlorate levels in drinking water below
their state limits, which are lower than the proposed MCL and
alternative MCLs. Therefore, the UCMR 1 sampling results from systems
in these states do not reflect the current occurrence and exposure
conditions. For the purpose of estimating the costs and benefits of the
proposed rule, the EPA assumed that no additional monitoring and
treatment costs would be incurred by the systems in the States of
California and Massachusetts. Systems in California account for some of
the perchlorate measurements reported below. The notes in the tables
below indicate whether results include or exclude systems in California
and Massachusetts.
    To update the occurrence data for systems sampled during UCMR 1
from the States of California and Massachusetts, the EPA identified all
systems and corresponding entry points which had reported perchlorate
detections in UCMR 1. Once the systems and entry points with detections
were appropriately identified, the EPA then used a combination of
available data from Consumer Confidence Reports (CCRs) and perchlorate
compliance monitoring data from California (https://sdwis.waterboards.ca.gov/PDWW/) and Massachusetts (https://www.mass.gov/service-details/public-water-supplier-document-search) to
match current compliance monitoring data (where available) to the
corresponding water systems and entry points sampled during UCMR 1.
    Out of the 540 detections previously described the EPA updated data
for 321 detections (320 from California systems and 1 from a
Massachusetts system). The convention used by the EPA to accomplish the
substitution of data was to match entry points with compliance data for
active entry points based on most recently reported compliance
monitoring data, if more than one data point was reported for an entry
point, the assigned value is an average of the annual monitoring
results at the entry point. In cases were the EPA could not find
updated entry point data, then the original data from UCMR 1 for such
entry point was kept.

                                   Table VI-1--UCMR 1 Data Summary Statistics
----------------------------------------------------------------------------------------------------------------
                                                                   Small system    Large system
                              Item                                    sample          census            Sum
----------------------------------------------------------------------------------------------------------------
Total samples...................................................           3,295          30,837          34,132
    Sample measurements >=4 [mu]g/L.............................              15             525             540
    Sample measurements >18 [mu]g/L.............................               1              16              17
    Sample measurements >56 [mu]g/L.............................               0               2               2
    Sample measurements >90 [mu]g/L.............................               0               1               1
Total entry points..............................................           1,454          13,482          14,936
    Entry points at which measurements >=4 [mu]g/L..............               8             328             336
    Entry points at which measurements >18 [mu]g/L..............               1              16              17
    Entry points at which measurements >56 [mu]g/L..............               0               2               2
    Entry points at which measurements >90 [mu]g/L..............               0               1               1
Total systems...................................................             797           3,068           3,865
    Systems at which measurements >=4 [mu]g/L...................               8             141             149
    Systems at which measurements >18 [mu]g/L...................               1              14              15
    Systems at which measurements >56 [mu]g/L...................               0               2               2
    Systems at which measurements >90 [mu]g/L...................               0               1               1
----------------------------------------------------------------------------------------------------------------
Source: (USEPA, 2019b). The total row counts and counts of measurements >=4 [mu]g/L identify all instances where
  perchlorate was detected at or above the minimum reporting level, including water systems in California and
  Massachusetts, which account for 537 systems in total and 51 systems at which measurements >=4 [mu]g/L. The
  instances where perchlorate measurements equal or exceed either 18 [mu]g/L, 56 [mu]g/L, or 90 [mu]g/L exclude
  results from California and Massachusetts because water systems in these States must meet limits below 18
  [mu]g/L. The small system counts reflect sample results that have not been extrapolated to small systems
  nationwide.

    Table VI-2 shows the service populations that correspond with the
occurrence summary in Table VI-1. The entry point population estimates
reflect the assumption that system population is uniformly distributed
across entry points; e.g., the entry point population for a system with
two entry points is one-half the total system population.

                          Table VI-2--UCMR1 Data Service Population Summary Statistics
----------------------------------------------------------------------------------------------------------------
                                                                   Small system    Large system
                              Item                                    sample          census            Sum
----------------------------------------------------------------------------------------------------------------
Total entry point population....................................       2,760,570     222,853,101     225,613,671
    Population served by entry points at which measurements >=4            9,484       4,281,937       4,291,420
     [micro]g/L.................................................
    Population served by entry points at which measurements >18            2,155         618,406         620,560
     [micro]g/L.................................................
    Population served by entry points at which measurements >56                0          32,432          32,432
     [micro]g/L.................................................
    Population served by entry points at which measurements >90                0          25,972          25,972
     [micro]g/L.................................................
Total system population.........................................       2,760,570     222,853,101     225,613,671
    Population served by systems at which measurements >=4                13,483      16,159,082      16,172,565
     [micro]g/L.................................................
    Population served by systems at which measurements >18                 4,309         696,871         701,180
     [micro]g/L.................................................
    Population served by systems at which measurements >56                     0          64,733          64,733
     [micro]g/L.................................................
    Population served by systems at which measurements >90                     0          25,972          25,972
     [micro]g/L.................................................
----------------------------------------------------------------------------------------------------------------
Source: (USEPA, 2019b). The populations for entry points/systems with measurements >=4 [micro]g/L identify all
  instances where perchlorate was detected at or above the minimum reporting level, including water systems in
  California and Massachusetts, which account for 39.6 million of the 225.6 million total population in UCMR 1,
  and 1.9 million of the 4.3 million population served by entry points at which measurements >=4 [micro]g/L. The
  instances where perchlorate measurements equal or exceed either 18 [micro]g/L, 56 [micro]g/L, or 90 [micro]g/L
  exclude results from California and Massachusetts because water systems in these States must meet limits below
  18 [micro]g/L. The small system counts reflect sample results that have not been extrapolated to small systems
  nationwide.

    As shown in the tables, 149 systems serving 16.2 million people had
measured levels of perchlorate greater than the minimum reporting
level. However, many of these systems have several entry points with no
measured levels of perchlorate greater than the minimum reporting
level; at the entry point level, the exposed population is
approximately 4.3 million people served by 336 entry points. Because
the uniform population distribution assumption may over or
underestimate the service population of any particular entry point, the
entry point estimates are uncertain. The system population estimates
serve as upper bounds on exposure.
    The EPA used entry point maximum measurements to estimate potential
baseline occurrence and exposure at levels that exceed the proposed MCL
and alternative MCLs. The maximum measurements indicate perchlorate
levels that occurred in at least one quarterly sample among surface
water systems and at least one semi-annual sample among ground water
systems.