or pesticide use information within any given exposure scenario from
which to judge or compensate for the biases introduced by the sampling
selection design being proposed (see also the last comment in our
response to Charge #1).

Recommendations Regarding AEATF

The AEATF Study Plan is dealing with a very different situation and is
much more amenable to experimental control.  In particular, it should be
feasible to increase the sample size for any scenario at a future date
if more observations are needed.  The proposal to take 15 monitoring
units initially is adequate to give an overview.  For probabilistic
assessments and determination of exposure at extreme upper percentiles,
15 units will not be enough.

The Panel was also asked whether the AEATF study should be conducted at
one simulated site or three field locations.  The simplest way to answer
this is to try both options a few times in a pilot study and compare the
results.  Perhaps three field sites should be treated as blocks or
strata rather than clusters.  In summary, the most important difference
between the two studies is the possibility of increasing the sample size
at a future date.

REFERENCES

  SEQ CHAPTER \h \r 1 Brouwer DH, Boeniger MF, Van Hemmen J.  2000.
Hand Wash and Manual Skin Wipes.  Ann. Occup. Hyg. 44(7):501-510.

Durkin P, Hertzberg R, Diamond G.  2004.  Application of PBPK Model for
2,4-D to Estimates of Risk in Backpack Applicators.  Environmental
Toxicology and Pharmacology 16:73-91.

  SEQ CHAPTER \h \r 1 Kline AA, Landers AJ, Hedge A, Lemley AT, Obendorf
SK, Dokuchayeva T.  2003.  Pesticide exposure levels on surfaces within
sprayer cabs.  Applied Engineering in Agriculture 19(4):397-403.

Lu C, Anderson LC, Morgan MS, Fenske RA.  1997.  Correspondence of
salivary and plasma concentrations of atrazine in rats under variable
salivary flow rate and plasma concentration.  J. Toxicol. Environ.
Health 52:317-329.

Lu C, Rodríguez T, Funez A, Irish R, Fenske RA.  2006.  The assessment
of occupational exposure to diazinon in Nicaraguan plantation workers
using saliva biomonitoring.  Ann. New York Acad. Sci. 1076:355-365.

Lu C, Rodriguez T, Thetkathuek T, Funez A, Pearson M.  2007.  Using
Salivary Biomarker in Exposure and Risk Assessments for Organophosphorus
Pesticides: Possibilities and Pitfalls. (submitted).

McDougal JN and Boeniger MF.  2002.  Methods for assessing risks of
dermal exposures in the workplace.  Critical Reviews in Toxicology
32:291-327.

Williams PL, Thompson D, Qiao G, Monteiro-Riviere N, Riviere JE.  1996.
The use of mechanistically defined chemical mixtures (MDCM) to assess
mixture component effects on the percutaneous absorption and cutaneous
disposition of topically exposed chemicals.  Toxicology and Applied
Pharmacology 141:487-496.

APPENDICES

  SEQ CHAPTER \h \r 1 Appendix A:  Definitions and Abbreviations

This glossary of terms was prepared not only because terminology is so
important within the topic of dermal hazards from chemicals but more
specifically to help assure that the responses of the Panel are both
internally consistent and properly interpreted.  Most of the terms in
this glossary come from the Review of Worker Exposure Assessment Methods
document prepared for the Panel by the U.S. Environmental Protection
Agency, Health Canada, and the California Environmental Protection
Agency (referred to herein as the "Review").

Absorption

  Factor  = 	A measure of the flux or amount of chemical that crosses a
biological boundary such as the skin (% of the total exposure that is
absorbed).   p.38

    SEQ CHAPTER \h \r 1 AHED  = 	Agricultural Handlers Exposure Database
to be developed by the AHETF.

  BHED  = 	Biocide Handlers Exposure Database to be developed by the
AEATF.

  bias  =  	One form of a bias is a consistent or overall difference
between the result and the true value being estimated, sometimes called
a systematic bias.  Results from a known bias (such as sample recovery
efficiency or representative measurements) can be adjusted for by a
simple calculation.  A bias exists for virtually all of the dosimetry
and biomonitoring methods discussed in this report.

Another form of bias is in sample selection in which some members of the
population are more likely to be included than others.  While the
existence of sample selection bias can be identified, its effect and
methods to adjust the results are not always known.

biological

monitoring  =	[BM or biomonitoring] "is usually employed to quantify the
absorbed dose (also referred to as body burden)."  The Agency accepts
monitoring data based on the collection of biological media such as
urine or blood.  Biomarker data can also be used for predicting
exposures.  In addition to biological sampling media, the Agency also
requires that additional supporting pharmacokinetics and/or
pharmacodynamic information be submitted that can be used to develop
exposure and/or risk estimates. p. 35.  For the purposes of this
discussion, biomonitoring was restricted to urinary excretion.

By definition, dose estimates resulting from biological monitoring
"integrate exposure across all routes.  Such an estimate of absorbed
dose, which avoids potential confounding from assumptions of dermal
penetration or inhalation retention, may be more useful in assessing
risk than route-specific doses estimated from passive dosimetry."
p.41-42

BM  =	See biological monitoring.

  SEQ CHAPTER \h \r 1 CV  = 		coefficient of variation calculated as the
standard deviation divided by the group's mean (sometimes also called a
coefficient of variance).

Dose  =	the amount of chemical absorbed from exposure to a pesticide in
a given scenario.  p. 21

Dermal

  exposure  =	defined by U.S. EPA (1996b) as the process of pesticide
residue deposition onto the skin, as well as the measurement of the
deposited residue.  p.38

Exposure  = 	Amount deposited on the surface of the skin that is
available for dermal absorption or amount that is inhaled, also referred
to as potential dose.  p. 21  See also Dermal exposure.

Factor-set  = 	a large number or combination of factors that
characterize a specific setting, e.g., climatic conditions, combinations
of equipment, task times, etc.  As used in Appendix C, each unique
combination of factors is denoted by the symbol Si.

  SEQ CHAPTER \h \r 1 inner

  dosimeter  =a dosimeter worn under the handler's work clothing and
held in some way against the skin.

LOAEL  =	Dose level in a toxicity study, the lowest dose level where an
adverse effect occurred in the study (mg pesticide active ingredient/kg
body weight/day).  p.38

MOE  =	Margin of exposure, value used by the Agency to represent risk or
how close a chemical exposure is to being a concern (unitless).   p.38

  SEQ CHAPTER \h \r 1 MU  =		Monitoring Unit or one person whose
exposure is assessed via measurements such as biomonitoring or passive
dosimetry.

NOAEL  =	Dose level in a toxicity study, where no observed adverse
effects occurred in the study (mg pesticide active ingredient/kg body
weight/day).  p.38

  SEQ CHAPTER \h \r 1 outer

  dosimeter  =	a dosimeter worn outside of regular work clothing (in
some cases, the regular work clothing has comprised the outer
dosimeter).

passive

  dosimetry  =	[PD] "employs some sort of physical monitor that traps
residues from the surface of the skin (i.e., they absorb or remove, such
as a dosimeter or a wash) to determine dermal exposure (i.e., also
referred to as a potential dose).  Passive dosimetry methods (e.g.,
patches, gloves, dosimeters or washes).  p. 35  For the purposes of this
discussion, passive dosimetry [PD] included hands or face/neck washing,
patch dosimeters, and whole-body dosimeters.

Patches  =	Various forms of absorbent pads (usually made of gauze but
sometimes of alpha-cellulose) placed on the body at fixed locations.

PD  =	see Passive Dosimetry.

PHED  =	Pesticide Handlers Exposure Database consists of dermal and
inhalation exposure measurements compiled by EPA beginning in the late
1980s from a wide variety of scenario specific pesticide handler
exposure studies.

Probable

  uncertainty =The expected diversity of the result based on an analysis
using propagation of error theory.  Its magnitude is based on the
precision of the individual measurements used to calculate a result and
the form of the calculations being made.  For linear calculations,
probable uncertainty may be characterized by a standard deviation,
coefficient of variation, or geometric standard deviation after
adjusting for the multiplication or division factor used to achieve the
result, such as after adjusting for sample recovery from a dosimeter or
in urine.

  variability  =	The diversity of the population being studied.  The
true variability of a population is only known if repeated measures of
whatever is being studied can be made with an accurate and completely
precise method.  Variability is usually characterized in terms of the
observed variation or geometric standard deviation of a random sample of
such measurements.

WBD  =	A whole-body dosimeter preferably composed of full-length cotton
underwear, cf., a WBD composed of coveralls and worn as outer clothing.
The latter suffers the combined problem of not retaining all of the
pesticide deposited onto them and passing through them some fraction of
the pesticide that was deposited.

Appendix B: Expanding the Concept of Grading Data

The current data grade (p. 31 of the Review) is based (in part) on two
relevant criteria: the "% Lab recovery" and the "CV for Lab recovery"
which is also a percent.  The current “Grading” evaluation of PHED
data is based on these two criteria independently, i.e., a lower
letter-grade is assigned if the data exceed either criterion.  The
effect of this interpretation is shown by the small shaded rectangles in
Figure B-1 below.  In fact, these two criteria are two characteristics
of the same data that interact and can be combined within the concept of
“data grade” to indicate the magnitude of the probable uncertainty
within the resulting exposure value, as calculated by the equation
below.  The “magnitude of uncertainty” is shown as a multiplying
factor herein but could be viewed as a percent by subtracting one and
multiplying by 100 (as a percent, it would be analogous to a CV, but
interpreting values that exceed several hundred percent was deemed
easier as a multiplier such as (4( than as the equivalent (500%).

 	Eqn. B.1

Figure B-  SEQ Figure \* ARABIC  1 .  A narrow view of graded data
quality

Each of these two criteria can contribute to the magnitude of the
uncertainty within the resulting calculated exposure; however, only
together do they define the magnitude of that statistically probable
uncertainty.  Figure B-2 shows an array of possible values of these two
criteria that have the same magnitude of probable uncertainty
represented by the relatively large shaded triangles comprising each
grade.  One can discern several points from this figure.  One is the
broader range of possible criteria that result in the same quantitative
effect on the probable uncertainty as an indicator of data quality.  The
Agency could consider broadening their criteria for "Data Grade" by
using the above equation without decreasing the precision of their
resulting data.

Figure B-  SEQ Figure \* ARABIC  2 .  A broader view of graded data
quality

Second, one can see in Figure B-2 that the CV of the Recovery has a
greater effect on the Grade than the mean Lab. Recovery.  In fact, one
can see that a sufficiently precise estimate of a poor sample recovery
can yield a smaller probable uncertainty in exposure than an imprecise
estimate of a good sample recovery.  An extension of this observation is
that the reliability of the result (as characterized by the magnitude of
the probable uncertainty) cannot be defined by only specifying a mean
recovery (any value on the X-axis in either figure) without also
specifying a CV.

Figure B-  SEQ Figure \* ARABIC  3 .  The impact of the CV of the mean
Lab. Recovery on

the magnitude of Probable Uncertainty

This observation is perhaps more visible in Figure B-3 in which the
Y-axis is the probable uncertainty and the data's CV is a parameter.
The Agency’s current definition of Grade is still depicted as the
shaded zones in the lower right, and the suggested extended definition
is shown as horizontal dashed lines at constant values of probable
uncertainty.  Notice that one cannot predict the variability of the
result by only specifying a value on the X-axis (a % Lab. Recovery)
without also specifying a CV.  Thus, the Agency should consider adding
CV limits to their Field Recovery and Storage Stability criteria.

A third observation is that the magnitude of the probable uncertainties
shown in these figures (even using the Agency's worst current Data Grade
of "D") are all much smaller than the range of variabilities in the data
discussed within their Review (such as a 3× range in dermal absorption
or differences between passive dosimetry and biomonitoring).  The
magnitude of these probable uncertainties will also be seen to be much
smaller than other uncertainties when the same principle behind Equation
B-1 is applied to passive dosimetry data in Charge #2 and to urinary
biomonitoring data in Charge #3.  The same principle (with less specific
measures of CV) could also be applied to other data attributes such as
the lack of complete body monitoring in much of the PHED dosimetry data,
the variability within the calculated results implicit within each form
of passive dosimetry, and the impact of the simplistic method currently
used to account for undetectable samples (due to censoring).  Any one of
these quantitative attributes of the data within PHED probably has a
greater impact on the variability within the results than does sample
recovery but are currently not part of the grading criteria.
Furthermore, incorporating these other measures of data quality into the
“data grade” is likely to illuminate the poor quality of much of the
existing PHED data and the potential for the new AHED data to improve
that quality by orders of magnitude.

Appendix C:  A Critique of the AHETF Study Design

The Panel’s understanding of risk assessment is that the exposure
value used in the risk equation is expected to be “representative”
of the average exposure that would be experienced by the population
potentially exposed to the chemical.  For probabilistic risk
assessments, individual exposure values are drawn from a distribution of
exposures that are expected to describe the distribution of long term
average exposures for individuals in the population potentially exposed
to an active ingredient.  The Agency should look at the proposed
sampling design through the lens of its “representativeness.”

The first assumption made (by AHETF) is that a surrogate cluster
sampling model that assumes underlying random selection can be used to
estimate sample sizes even though the proposed sampling methodology does
not advocate random sampling for clusters.  The second assumption (by
AHETF) relates to the normality of variance components in the
nested-effects linear model on log-normalized exposure.  The real
concern herein is with the acceptability of using the surrogate random
sampling model.

The discussion in Sections 5.1 and 5.2 of the AHETF Technical Summary
background document (AHETFa, 2006) is excellent in that it provides a
good framework for thinking about sampling for exposure assessment.  The
following will use a slight modification of their conceptual model to
illustrate a technical concern with the sampling protocol that is being
proposed.

The goal of the AHED dataset is the estimation of the true exposure, E
for a specific handler task. To collect these data, AHETF proposes a
cluster sampling or hierarchical sampling design in which clusters (or
studies) are essentially examinations of handlers performing the handler
task of interest at specific locations and times.  As mentioned in the
background documentation, there exist a very large number of potential
studies.

Figure C-1.  AHETF proposed cluster sampling design

Conceptually each Ci is characterized by specific settings for a large
number of factors, e.g., climatic conditions, equipment combinations,
task times, etc.  Denote each unique combination of factors by Si for
factor-set.  (Note: in the discussion before the Panel, the word
“scenario” was used for “factor-set,” but this caused some
confusion and has been changed here.  The word scenario as used by AHETF
and EPA applies to a handler task.)  In theory, if one knew all of the
conditions that affected exposure one could compute a true average
exposure concentration Ei for each factor-set.  Each cluster/study is
essentially a replicate of some set of factors. Since many of the
factors that impact exposure are continuous, theoretically there are an
infinite number of factor-sets and hence there are an infinite number of
potential studies.

Figure C-2.  AHETF study design conceptualized as a set of factor
conditions

One way to think of the true exposure for a handler task within a
cluster would be as the average of exposures for the handler task across
all possible factor-sets.

  								Eqn. C.1

But this equation assumes that each possible factor-set has an equal
probability (or frequency) of occurrence, and we know this is not true.
Pest application tasks have certain climatic conditions that define when
they must or can be performed.  Some types of equipment are more common
than others.  For example, the factors-set contained within the blue
circle in Figure C-2 might represent the more common conditions.  If one
knew the relative frequency or probability of each factor-set, denoted
as Pi, then the true exposure could be estimated as a weighted average.

  					Eqn. C.2

Since the true condition space is continuous, the most mathematically
appropriate way to describe the true exposure is given in the integral
part of Equation C.2.  The term dF(s) essentially describes the relative
probability for each possible factor-set, s, in the total factor space
S.

What does the surrogate random sampling model mean in terms of clusters
C and factor-set S?  With a random selection of clusters, one
essentially selects factor-sets at random for inclusion in the study,
and their proportion within the sample will be their relative frequency
within the population of interest.  This also means that averaging the
study-specific average exposures should produce an unbiased estimate of
the true handler task exposure.  That is, Equation C.3 is an unbiased
estimate of what we need for the risk assessment.

  						Eqn. C.3

From Figure C-2, simple random sampling would result in relatively more
of the sampling effort placed in the blue circle than would be placed
outside of it.

Now consider the “Diversity” sampling approach as proposed by AHETF.
 The approach does not include randomness.  Through thoughtful
considerations of location and time, it is possible that a large number
of factor-sets will be examined.  In fact, the background document to
the Panel seemed to indicate that the locations and times would be
selected to ensure that different conditions would be represented.  The
problem with the Diversity approach is that the relative frequency of
scenarios will not necessarily be considered in the selection of the
scenarios.  Hence, it is possible (if not likely) that only one of a
really common (high relative frequency) factor-set will be included in
the sample set at the same time as one really rare (low relative
frequency) factor-set is included.  The dots and clusters in Figure C-3
depict an array of sampled conditions chosen for their diversity that
are not representative of the relative frequency of factor-sets.  The
relatively common factor conditions found within the blue circle has
small representation compared to the uncommon conditions outside the
blue circle.  When the sample average of estimated exposures is
computed, estimated exposure for the rare scenario is weighted equally
with the estimated exposure for the common scenario, and as a result the
sample average will be a biased estimate of the true exposure.

Figure C-3.  Non random sampling can result in samples that do not
properly represent the distribution of conditions

The issue is even worse if one wants to estimate a distributional
parameter of the true exposure like the standard deviation or an upper
percentile.  The non-probability sample will not produce a faithful
estimate of the population distribution.  Worse, one cannot even predict
the direction of the bias.  The study design would produce overestimates
if the over-represented rare factor-sets produce high exposures, or the
design would produce underestimates if over-represented rare factor-sets
produce low exposures.

This then is the basis for the statement made by the AHETF statistician
that “non-random sampling means that statistical methods alone are
insufficient for generalizing to the target population.”  Most
statisticians and many risk assessors are aware of this problem.  This
problem is not new.  Almost every environmental dataset has this
problem.  The question is whether we want to support the creation of
another environmental dataset with this problem.

AHETF acknowledges the above problems in the background document and
points out that rarely are the true relative frequencies of the
factor-sets known.  At the same time, it is not possible to create a
simple random sample that is guaranteed to appropriately represent a
specific scenario.  The goal of the “Diversity” sampling approach
proposed for populating AHED is to “achieve a diversity of major
factors that are likely to influence exposure” and to attempt to
“capture the major aspects of” the actual distribution of exposures.
 In essence, AHETF will attempt to identify specific Ci to sample that
are “representative” of the whole set of possible conditions such
that the distribution of exposures from the Diversity sample is
approximately equal to the distribution of exposures appropriately
weighted for all factor-sets.

Statisticians have heard this kind of sampling proposal many times but
have never seen a true success.  It is actually impossible to
purposively define a sample that produces a distribution of exposures
that duplicates the true population distribution when one has no
knowledge of the true population distribution to start with.  Rare
events are seldom given proper consideration and common events are often
under represented.  Selecting to get true representation does not work.
Randomness in selection must be used somewhere in the design to even
approach an unbiased estimate.

So, is this really a hopeless situation?  Not necessarily.  EPA and
AHETF have at this point the opportunity to rethink these issues and
possibly come up with some new approaches that might get them closer to
their stated goals.  While more creative thinking may come up with a
number of feasible alternatives, consider the following approach.

Create a list of all of the factors that are known to impact exposure
levels within a specific scenario.  The list may be long but is not
infinite.

Rank-order the factors by their expected magnitude of impact on exposure
variation.  A Delphi approach might be used with a Panel of expert risk
assessors to accomplish this ranking.

Select the top two to four factors, and identify for each factor two to
three categories or levels.

Create the set of all possible combinations of factor levels.  Consider
these combinations as strata of the population of interest.  In a sense,
these become the factor-sets of interest, Si*.

Next assign a weight, wi, to each factor-set, Si* that approximates its
relative frequency in the population.  Sampling theory tells us that
these weights don’t have to be exact for us to gain large improvements
in estimator precision.  Here again, the use of a Delphi approach with a
Panel of agricultural experts could help.

Selection of studies (two options).

Option 1 (see Figure C-4): Select at-random studies and/or MUs for each
factor-set such that the relative number of exposure estimates obtained
for the factor-set equals its weight.  The population exposure estimate
is the average of the estimated exposures for the MUs.

Option 2: (see Figure C-5): Select at random a fixed number of studies
or MUs for each factor set and assign each the factor set weight, wi.
The population exposure estimate is the weighted average of the
estimated exposures for the MUs.

Figure C-4.  Allocation of studies and MUs to factor-set strata
according to their relative weights.

Figure C-5.  Uniform allocation of studies and MUs to each factor-set
strata.

This approach incorporates both “representation” and
“randomness” into the creation of the database and should result in
an average exposure estimate that is less biased than the “average”
that would be obtained from the Diversity sampling protocol.

The above approach is quite similar to what AHETF is actually proposing.
 The major difference is that in the approach outlined here an attempt
will have been made first to map out the possible condition space in a
rough categorized way, to assign relative importance to each category,
and finally to sample according to that relative importance.

A number of issues with this approach remain to be addressed by the
Agency and AHETF statisticians.  For example, if a uniform sampling plan
(Option 2) were used, the sample weights would be used to estimate
simple statistics such as the mean or standard deviation.  Much more
complex is the use of these weights to estimate the upper percentiles
for exposure, to estimate the exposure distribution, or to test the
exposure distribution for a specific distributional form.

It was pointed out that many seemingly unrelated variables are
correlated in pesticide application studies (e.g., the number of acres
sprayed, type of PPE, use of a tractor with a cab, etc.).  It is also
acknowledged that sufficient information on condition factors would be
needed to understand how factors co-vary.  A two-dimensional
stratification based on Factor A and B would be much less effective if
Factors A and B were highly correlated.  It would be better to use two
factors that are relatively uncorrelated in the definition of the
strata.  In this case, each factor could be seen as a surrogate or
representative of a large set of correlated factors (kind of like a
principal component).

Finally, the interface between the User and the AHED dataset should
reflect not only the data contained within the database but also the
sampling design used to collect that data.  Hence, if a uniform sampling
design were used, weighted estimates and correspondingly appropriate
tests for distributions should be presented as a result of a User query.

 A similar but more vexing problem would result if the data showed that
newer equipment and modern techniques actually resulted in higher
exposures.

 While it is possible to measure that variability by cutting a WBD into
small pieces (about the size of a patch) and analyzing each piece
separately, such a study is more amenable to research than to a new data
requirement.

 Despite such a good fit and the fact that several Panelists noted that
KO/W is a part of other models for dermal absorption rate (although here
it is only modeling adsorption), an analysis of dermal recovery for only
two insecticides each at only two points in time (nominally zero and one
hour) is not sufficient to validate this model for all other chemicals.

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 skin and in particular have very different adsorbent and absorbent
properties than human skin, b) some pesticides cannot be analytically
recovered well from cotton, c) breakthrough can happen with gloves, d)
it may be difficult for applicators to do some tasks while wearing the
gloves, and e) wearing cotton gloves may modify the handler’s
behavior.

  Technically, Equation 3.1 only approximates the result of a
propagation of error analysis of the two steps of absorption and
excretion as only one step, but the result is not far off because the
fraction of the dermal exposure that is absorbed is generally much lower
than the fraction of the absorbed dose that is excreted.

 PAGE

 PAGE   2

OFFICE OF

PREVENTION, PESTICIDES, AND TOXIC SUBSTANCES


[Federal Register Volume 80, Number 51 (Tuesday, March 17, 2015)]
[Notices]
[Pages 13938-13940]
[FR Doc No: 2015-06009]

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SECURITIES AND EXCHANGE COMMISSION

[Release No. 34-74478; File No. SR-MIAX-2015-16]

Self-Regulatory Organizations; The Miami International Securities
Exchange LLC; Notice of Filing and Immediate Effectiveness of a
Proposed Rule Change To Amend Its Fee Schedule

March 11, 2015
    Pursuant to the provisions of Section 19(b)(1) of the Securities
Exchange Act of 1934 (``Act'') \1\ and Rule 19b-4 thereunder,\2\ notice
is hereby given that on February 27, 2015, Miami International
Securities Exchange LLC (``MIAX'' or ``Exchange'') filed with the
Securities and Exchange Commission (``Commission'') a proposed rule
change as described in Items I, II, and III below, which Items have
been prepared by the Exchange. The Commission is publishing this notice
to solicit comments on the proposed rule change from interested
persons.
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    \1\ 15 U.S.C. 78s(b)(1).
    \2\ 17 CFR 240.19b-4.
---------------------------------------------------------------------------

I. Self-Regulatory Organization's Statement of the Terms of Substance
of the Proposed Rule Change

    The Exchange is filing a proposal to amend the MIAX Options Fee
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    In its filing with the Commission, the Exchange included statements
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that are not a Priority Customer and Firms; and (ii) modify the
transaction fees for non-Priority Customers and Firms for achieving
certain Priority Customer Rebate Program volume tiers. The proposed
changes are based on the similar fees of other competing options
exchange.\3\
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    \3\ See NASDAQ OMX PHLX LLC Pricing Schedule, Section II; NYSE
Amex Options Fee Schedule, p. 6; Chicago Board Options Exchange,
Incorporated, Fee Schedule, p.1. See also Securities Exchange Act
Release No. 68556 (January 2, 2013), 78 FR 1293 (January 8, 2013)
(SR-BX-2012-074).
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    The Exchange proposes to increase the transaction fees for Public
Customers that are not a Priority Customer and Firms. Specifically, the
Exchange proposes to assess the following fees for transactions for
Public Customers that are not a Priority Customer: (i) $0.47 per
contract for standard options and $0.05 per contract for mini options
in Penny Pilot options classes; and (ii) $0.62 per contract for
standard options and $0.06 per contract for mini options in non-Penny
Pilot options classes. In addition, the Exchange proposes to assess the
following fees for transactions for Firms: (i) $0.37 per contract for
standard options and $0.04 per contract for mini options in Penny Pilot
options classes; and (ii) $0.42 per contract for standard options and
$0.04 per contract for mini options in non-Penny Pilot options classes.
    The Exchange proposes to continue to offer Public Customers that
are not a Priority Customer and Firms the opportunity to reduce
transaction fees by $0.02 per contract in standard options in both
Penny Pilot and non-Penny Pilot options classes.\4\ Specifically, any
Member or its affiliates of at least 75% common ownership between the
firms as reflected on each firm's Form BD, Schedule A, that qualifies
for Priority Customer Rebate Program volume tiers 3, 4, or 5 and is a
Public Customer that are not a Priority Customer will be assessed $0.45
per contract for standard options in Penny Pilot options classes and
$0.60 per contract for standard options in non-Penny Pilot options
classes. Further, any Member or its affiliates of at least 75% common
ownership between the firms as reflected on each firm's Form BD,
Schedule A, that qualifies for Priority Customer Rebate Program volume
tiers 3, 4, or 5 and is a Firm will be assessed $0.35 per contract for
standard options in Penny Pilot options classes and $0.40 per contract
in non-Penny Pilot options classes. The Exchange believes that these
incentives will encourage Public Customers that are not a Priority
Customer and Firms to transact a greater number of orders on the
Exchange.
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    \4\ See Securities Exchange Release Nos. 72988 (September 4,
2014), 79 FR 53808 (September 10, 2014) (SR-MIAX-2014-46); 72989
(September 4, 2014), 79 FR 53792 (September 10, 2014) (SR-MIAX-2014-
47).
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    The Exchange proposes to implement the new transaction fees
beginning March 1, 2015.
2. Statutory Basis
    The Exchange believes that its proposed rule change is consistent
with Section 6(b) of the Act \5\ in general, and furthers the
objectives of Section 6(b)(4) of the Act \6\ in particular, in that it
is an equitable allocation of reasonable fees and other charges among
Exchange members.
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    \5\ 15 U.S.C. 78f(b).
    \6\ 15 U.S.C. 78f(b)(4).
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    The Exchange's proposal to increase the transaction fees for Public
Customers that are not a Priority Customer and Firms is reasonable
because the Exchange's fees will remain competitive with fees at other
options exchanges.\7\ The Exchange's proposal to increase the
transaction fees for Public Customers that are not a Priority Customer
and Firms is equitable and not unfairly discriminatory because the
increase applies equally to all such market participants. The Exchange
does not assess Priority Customers transactions fees because Priority
Customer order flow enhances liquidity on the Exchange for the benefit
of all market participants. Priority Customer liquidity benefits all
market participants by providing more trading opportunities, which
attracts Market Makers and other market participants. An increase in
the activity of these market participants in turn facilitates tighter
spreads, which may cause an additional corresponding increase in order
flow from other market participants. Market Makers are assessed lower
transaction fees as compared to Public Customers that are not a
Priority Customer, Non-MIAX Market Makers, Non-Member Broker-Dealers,
and Firms because they have obligations to the market and regulatory
requirements, which normally do not apply to other market
participants.\8\ They have obligations to make continuous markets,
engage in a course of dealings reasonably calculated to contribute to
the maintenance of a fair and orderly market, and not make bids or
offers or enter into transactions that are inconsistent with a course
of dealings. In addition, charging non-members higher transaction fees
is a common practice amongst exchanges because Members are subject to
other fees and dues associated with their membership to the Exchange
that do not apply to non-members. The proposed differentiation as
between Public Customer that is not a Priority Customer, Firms, and
other market participants recognizes the differing contributions made
to the liquidity and trading environment on the Exchange by these
market participants.
---------------------------------------------------------------------------

    \7\ See NASDAQ OMX PHLX LLC Pricing Schedule, Section II; NASDAQ
Options Market LLC's Pricing Schedule, Chapter XV.
    \8\ See Exchange Rules 603 and 604.
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    The Exchange's proposal to offer Public Customers that are not a
Priority Customer and Firms the opportunity to reduce transaction fees
by $0.02 per contract in standard options, provided certain criteria
are met, is reasonable because the Exchange desires to offer all such
market participants an opportunity to lower their transaction fees. The
Exchange's proposal to offer Public Customers that are not a Priority
Customer and Firms the opportunity to reduce transaction fees by $0.02
per contract in standard options, provided certain criteria are met, is
equitable and not unfairly discriminatory because the Exchange will
offer all market participants, excluding Priority Customers, a means to
reduce transaction fees by qualifying for volume tiers in the Priority
Customer Rebate Program. The Exchange believes that offering all such
market participants the opportunity to lower transaction fees by
incentivizing them to transact Priority Customer order flow in turn
benefits all market participants.
    The Exchange believes that the proposal to allow the aggregation of
trading activity of separate Members or its affiliates for purposes of
the fee reduction is fair, equitable and not unreasonably
discriminatory. The Exchange believes the proposed rule change is
reasonable because it would allow aggregation of the trading activity
of separate Members or its affiliates for purposes of the fee reduction
only in very narrow circumstances, namely, where the firm is an
affiliate, as defined herein. Furthermore, other exchanges, as well as
MIAX, have rules that permit the aggregation of the trading activity of
affiliated entities for the purposes of calculating and assessing
certain fees. The Exchange believes that offering all such market
participants the opportunity to lower transaction fees by incentivizing
them to transact Priority Customer order flow in turn benefits all
market participants.
    The Exchange believes that its proposal to assess transaction fees
in non-Penny Pilot options classes, which differs from Penny Pilot
options classes, is consistent with other options markets

that also assess different transaction fees for non-Penny Pilot options
classes as compared to Penny Pilot options classes. The Exchange
believes that establishing different pricing for non-Penny Pilot
options and Penny Pilot options is reasonable, equitable, and not
unfairly discriminatory because Penny Pilot options are more liquid
options as compared to non-Penny Pilot options. Additionally, other
competing options exchanges differentiate pricing in the similar manner
today.\9\
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    \9\ See NASDAQ OMX PHLX LLC Pricing Schedule, Section II; NYSE
Amex Options Fee Schedule, p. 6; Chicago Board Options Exchange,
Incorporated, Fee Schedule, p. 1. See also Securities Exchange Act
Release No. 68556 (January 2, 2013), 78 FR 1293 (January 8, 2013)
(SR-BX-2012-074).
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B. Self-Regulatory Organization's Statement on Burden on Competition

    The Exchange does not believe that the proposed rule change will
impose any burden on competition not necessary or appropriate in
furtherance of the purposes of the Act. The proposal is similar to the
transaction fees found on other options exchanges; therefore, the
Exchange believes the proposal is consistent with robust competition by
increasing the intermarket competition for order flow from market
participants. The proposal more closely aligns the fees for Public
Customers that is not a Priority Customer and Firms to those of non-
MIAX Market Makers and non-Member Broker-dealers. To the extent that
there is additional competitive burden on non-member market
participants, the Exchange believes that this is appropriate because
charging non-members higher transaction fees is a common practice
amongst exchanges and Members are subject to other fees and dues
associated with their membership to the Exchange that do not apply to
non-members. To the extent that there is additional competitive burden
on market participants that are Public Customer not Priority Customers
or Firms, the Exchange believes that this is appropriate because the
proposal should incent Members to direct additional order flow to the
Exchange and thus provide additional liquidity that enhances the
quality of its markets and increases the volume of contracts traded
here. To the extent that this purpose is achieved, all the Exchange's
market participants should benefit from the improved market liquidity.
Enhanced market quality and increased transaction volume that results
from the anticipated increase in order flow directed to the Exchange
will benefit all market participants and improve competition on the
Exchange. The Exchange notes that it operates in a highly competitive
market in which market participants can readily favor competing venues
if they deem fee levels at a particular venue to be excessive. In such
an environment, the Exchange must continually adjust its fees to remain
competitive with other exchanges and to attract order flow. The
Exchange believes that the proposal reflects this competitive
environment.

C. Self-Regulatory Organization's Statement on Comments on the Proposed
Rule Change Received From Members, Participants or Others

    Written comments were neither solicited nor received.

III. Date of Effectiveness of the Proposed Rule Change and Timing for
Commission Action

    The foregoing rule change has become effective pursuant to Section
19(b)(3)(A)(ii) of the Act.\10\ At any time within 60 days of the
filing of the proposed rule change, the Commission summarily may
temporarily suspend such rule change if it appears to the Commission
that such action is necessary or appropriate in the public interest,
for the protection of investors, or otherwise in furtherance of the
purposes of the Act. If the Commission takes such action, the
Commission shall institute proceedings to determine whether the
proposed rule should be approved or disapproved.
---------------------------------------------------------------------------

    \10\ 15 U.S.C. 78s(b)(3)(A)(ii).
---------------------------------------------------------------------------

IV. Solicitation of Comments

    Interested persons are invited to submit written data, views and
arguments concerning the foregoing, including whether the proposed rule
change is consistent with the Act. Comments may be submitted by any of
the following methods:

Electronic Comments

     Use the Commission's Internet comment form (http://www.sec.gov/rules/sro.shtml); or
     Send an email to<EMAIL_ADDRESS>Please include
File Number SR-MIAX-2015-16 on the subject line.

Paper Comments

     Send paper comments in triplicate to Brent J. Fields,
Secretary, Securities and Exchange Commission, 100 F Street NE.,
Washington, DC 20549.

All submissions should refer to File Number SR-MIAX-2015-16. This file
number should be included on the subject line if email is used. To help
the Commission process and review your comments more efficiently,
please use only one method. The Commission will post all comments on
the Commission's Internet Web site (http://www.sec.gov/rules/sro.shtml). Copies of the submission, all subsequent amendments, all
written statements with respect to the proposed rule change that are
filed with the Commission, and all written communications relating to
the proposed rule change between the Commission and any person, other
than those that may be withheld from the public in accordance with the
provisions of 5 U.S.C. 552, will be available for Web site viewing and
printing in the Commission's Public Reference Room, 100 F Street NE.,
Washington, DC 20549, on official business days between the hours of
10:00 a.m. and 3:00 p.m. Copies of such filing also will be available
for inspection and copying at the principal office of the Exchange. All
comments received will be posted without change; the Commission does
not edit personal identifying information from submissions. You should
submit only information that you wish to make available publicly. All
submissions should refer to File Number SR-MIAX-2015-16 and should be
submitted on or before April 7, 2015.
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    \11\ 17 CFR 200.30-3(a)(12).

    For the Commission, by the Division of Trading and Markets,
pursuant to delegated authority.\11\
Brent J. Fields,
Secretary.
[FR Doc. 2015-06009 Filed 3-16-15; 8:45 am]
BILLING CODE 8011-01-P


MEMORANDUM
To: 		Docket EPA-HQ-OAR-2023-0072
From: 		U.S. EPA
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Subject: 	Recent Developments in Emissions Controls and the Electric Power Sector References
--------------------------------------------------------------------------------

THE FOLLOWING ARE REFERENCES THE U.S. Environmental Protection Agency (EPA) cited in section IV (Recent Developments in Emissions Controls and the Electric Power Sector) of the preamble of the proposed New Source Performance Standards for Greenhouse Gas Emissions from New, Modified, and Reconstructed Fossil Fuel-Fired Electric Generating Units; Emission Guidelines for Greenhouse Gas Emissions from Existing Fossil Fuel-Fired Electric Generating Units; and Repeal of the Affordable Clean Energy Rule.