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                                         CHAPTERS
                          DESCRIPTION OF DATA CONVENTIONS
 This chapter discusses the types of data in the TEC analytical database and the hierarchy and
 procedures for aggregating multiple sampling observations within a sampling day.

\                 ,   ,,                 .  '                    . ,                    ,
 3.1 Data Review

 The EPA wastewater sampling data in the analytical database were thoroughly reviewed and validated
 by EPA's Sample Control Center (SCC).  During this review, the integrity_of each sample Was assessed
 to ensure that all specifications of the sampling protocol were met. The reviewers determined that
 some samples should be excluded from the analyses. Samples with flags of "EXCLUDE" or
 "DETECTED," which indicate that a value was detected but the concentration value was not recorded,
 were excluded from analyses.

 Also during the data  review, some samples were qualified with a greater than (>) sign, indicating that
 the reported concentration value is considered a lower limit of the actual value.  This is because the
 reported concentration was outside the range of the analytical method. When possible, these samples
 were diluted and reanalyzed. Otherwise tiiese samples were handled as right-censored samples and
 excluded from all calculations.
 3.2 Data Types

 The TEC analytical database, developed from the SCC, contains the following three different types of
 samples delineated by certain qualifiers in the database:

    •   Non-censored (NC): a measured value, i.e., a sample measured above the level at which the
        detection decision was made.

    •   Non-detect (ND):  samples for which analytical measurement did not yield a concentration
        above the sample-specific detection limit.

    •   Right-censored (RC):  these samples were qualified with a greater than (>) sign, signifying
        that the reported value is considered a lower limit of the actual concentration. All RC values
        were excluded from analyses because these values could not be quantified with certainty.
3.3  Data Modifications

For some pollutants it was necessary to modify the reported concentrations prior to aggregating daily
samples. One modification was made to concentrations reported below the Method Detection Limit
(MDL).  If a pollutant was not classified in the Metals chemical group, then any concentrations
reported as less than the MDL were set equal to the MDL and labeled as ND.  Also, modifications to
the concentrations hi the Truck/Chemical Direct and Rail/Chemical Direct subcategories were
necessary since data were available only for indirect discharging facilities. The demonstrated biological

                        -                     3-1                            '         '

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treatment percent removal was applied to the untreated concentrations using the following equation:
                           Final Cone. =  Cone.  * (100-%Removal)


If the final concentration was less than the MDL it was set equal to the MDL. If no additional percent
removal was provided by the biological treatment unit, the reported concentration was used.

3.4 Data Aggregation

Data aggregation for the TEC analj^tical data was performed at two levels. This section discusses the
different levels and approaches for data aggregation,  including multiple grab samples (one or more
samples collected for a particular sampling point over time, assigned different sample numbers, and
could not be physically composited) and field duplicates (one or more samples collected for a particular
sampling point at approximately the same time, assigned different sample numbers, and flagged as
duplicates for a single episode number).

3.4.1 Data Aggregation Across Multiple Grab Samples

The first type of data aggregation performed was for  multiple grab samples. Within the TEC database,
Hexane Extractable Material (HEM) and Silica Gel Treated-Hexane Extractable Material (SGT-HEM)
were reported as concentrations of multiple grab samples taken during one-day sampling periods.
Since LTAs and limitations were based on daily concentrations, multiple observations on a single day at
the same sample point were averaged. When all of the samples hi a set were NC, i.e., detected
samples, the arithmetic average of the samples was straightforward. However, when one or more of
the samples were not detected, or ND, multiple grab  samples were aggregated within each sampling
day/sample point combination using the methods identified in Table 3-1.

                                          Table 3-1.
                        Method for Averaging Multiple Grab Samples

If Observations are:
A11NC
A11ND
NCandND
1. Max. NC >
Max. Detection Limit
2. Max. NC <;
Max. Detection Limit

Label of "Average" .
NC
ND

NC ,


ND

Value of "Average5"
SNQ/n
Maximum Detection Limit

(SNCj +SND.)/n


Max. Detection Limit
        n = number of grab samples per day
                                            3-2

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3.4.2 Aggregation of Field Duplicates

Another type of data aggregation for the TEC data was performed due to the identification of field
duplicates in the database.  The field duplicates are defined.as one or more samples collected for a
particular sampling point at approximately the same time, assigned different sample numbers, and
flagged as duplicates for a single episode number/sampling point.  Duplicates were collected for
purposes of quality assurance/quality control. Table 3-2 presents the methods used to aggregate
duplicates.

                                          Table 3-2.
                        Method for Averaging Field Duplicate Samples
* **" t * *" -^ •** ^
-* iMObsejrv-ationsarej^ "Xs
Both NC
Both ND
NCandND
1. NO Detection Limit
2. NC < Detection Limit
> * i"* i * ^ %!
» Label of f Average**^
. NC
ND
NC
ND
v \S *'J**i> ~ ! ~ * ?'f l^#
- fc '* "Value~of/*ATeragew _."
SNCf/2
Maximum Detection Limit
(NC. + ND)/2
Detection Lunit
  NC = non-censored values
  ND = non-detected values                                                          _

If a sample had both multiple grabs and field duplicates, the multiple grabs were aggregated first.
Listings of daily data and summary statistics following aggregation of grabs and field duplicates are
presented in Appendices A.I - A.7 and B.I - B.7, respectively.
                                             3-3 .

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                                        CHAPTER 4
       STATISTICAL METHODOLOGY: MODIFIED DELTA-LOGNORMAL MODEL
4.1 Basic Overview of Delta-lognonnal Distribution

The lognofmal distribution is often appropriate for modeling effluent data.  However, the presence of
NDs and very low concentration measurements in the TEC effluent data led to the consideration of a
modification to the lognormal distribution in modeling such data for several reasons.  First, the
lognormal model assumes that all concentration values are positively-valued.  Second, the actual values
of NDs are not known, though each ND has a concentration somewhere between zero and the reported
detection limit.  In this sense, ND measurements represent, in statistical terms, what are known as
censored samples.

In general, censored samples are measurements for which the exact value is not known but are bounded
either by an upper or lower numerical limit. Nbn-detects qualify in this framework as left-censored
samples, which have an upper bound at the detection limit and a lower bound at zero. To model NDs
as left-censored samples under a strictly lognormal density model, it is necessary to assume that the
exact (but unknown) values of these measurements follow the same lognormal distributional pattern as
the rest of the detected measurements and that they are positively-valued (i.e., greater than zero).

Therefore, two reasonably simple modifications to the lognormal density model have been used by EPA
for several years.  The first modification is known as the classical delta-lognormal model (Figure  4-1),
first used hi economic analysis to model income and revenue patterns (see Atchison and Brown, 1955).
In this adaptation of the simple lognormal density, the model is expanded to include zero amounts. To
do this, all positive (dollar) amounts are grouped together and fit to a lognormal density. Then all zero
amounts are segregated into another group of measurements representing a  discrete distributional
"spike" at zero. The resulting mixed distribution, combining a continuous  density portion with a
discrete-valued spike, is known as the delta-lognormal distribution. The delta in the name refers to the
percentage of the overall distribution contained in the spike at zero; that is,  the percentage of zero
amounts.

                                        Figure 4-1.
                                  Delta-lognormal Model
    Non-Detects
                                            4-1

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Researchers at EPA (see Kahn and Rubin, 1989) further adapted the classical delta-lognormal model
("adapted model") to account for ND measurements in the same fashion that zero measurements were
handled in the original delta-lognormal. Instead of zero amounts and non-zero, positive amounts, the
data consisted of NDs and detects.  Rather than assuming that NDs represented a spike of zero
concentrations, these samples were allowed to have a single positive value, usually equal to the
minimum level of the analytical method (Figure 4-2).  Since each ND was assigned the same positive
value, the distributional spike hi this adapted model was located not at zero, but at the minimum level.
This adaptation is appropriate since it is known  that the NDs are some value greater than zero.  This
adapted model was used hi developing limitations for the Organic Chemicals, Plastics, and Synthetic
Fibers (OCPSF) and pesticides manufacturing rulemaking.

                                          Figure 4-2.
                             .  Adapted Delta-lognormal Model
       Non-Defects
             0 61016 20
In the adapted delta-lognormal model, the delta again referred to those measurements contained hi the
discrete spike, this time representing the proportion of ND values observed within the data set.  By
using this approach, computation of estimates for the population mean and variance could be done
easily by hand, and NDs were not assumed to follow the same distributional pattern as the detected
measurements. The adapted delta-lognormal model can be expressed mathematically as follows:
               Pr (Uzu)  =
(1-8) $ [(log(«) - ji)/a]       if   0< M < D
 8 + (1 -8) $ [(log(D) - n)/q]   i/  M  = D
 8 + (1 -8) * [(log(M) - |i)/0J   if  u> D
(4.1)
where 8 represents the true proportion of NDs (or the probability that any randomly drawn
measurement will be an ND), D equals the minimum level value of the discrete spike assigned to all
NDs, <&(•) represents the standard normal cumulative distribution function, and p and a are the
parameters of the lognormal density portion of the model. This model assumes that all non-detected
values have a single detection limit I).            .                       .

It is also possible to represent the adapted delta-lognormal model in another mathematical form, one hi
which it is particularly easy to derive formulas for the expected value (i.e., LTA) and variance of the
model.  In this case, a random variable distributed according to the adapted delta-lognormal distribution
can be represented as the stochastic combination of three other independent random variables.  The
                                             4-2

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first of these variables is an indicator variable, !„, equal to one when the measurement u is an ND and
equal to zero when u is a detected value. The second variable, XD, represents the value of an ND
measurement (discrete). In the adapted delta-lognormal, this variable is always a constant equal to the
concentration value assigned to each ND (i.e., equal to D in the adapted delta-lognormal model),  In
general, however, XD need not be a constant, as will be seen below in the modified delta-lognormal
model.  The final random variable, Xc, represents the value of a detected measurement and is
distributed according to a lognormal distribution (continuous) with parameters fi and a.

Using this formulation, a random variable from the adapted delta-lognormal model can be written as

                                                                                          (4.2)
arid the expected value of U is then derived by substituting the expected value of each quantity in the
right-hand side of the equation.  Because the variables !„,• XD, and Xc are mutually independent, this
leads to the expression
             E(U)  = dE(XD)+(l-d)E(:Xc)  = 8D  + (l-8)exp(n + 0.5 o2)
                                                                                          (4.3)
where again 8 is the probability that any random measurement will be ND and me exponentiated
expression is the familiar mean of a lognormal distribution. In a similar fashion, the variance of the
adapted delta-lognormal model can be established by squaring the expression for U above, taking
expectations, and subtracting the square of E(U) to get
Var(U) = E(U2) - [E(U)-f =
                             =
                                                                                          (4.4)
Since, in the adapted delta-lognormal formulation, XD is a constant, this expression can be reduced to
the following:
    Var(U)  = (l-5)exp(2(H-o2)[exp(o2)--(l-5)] ,+ 8(1-8)£>[.D-2exp(|i+0.502)].
                                                                                       (4.5)
In order to estimate the adapted delta-lognormal mean and variance from a set of observed sample
measurements, it is necessary to derive sample estimates for the parameters 8, fi, and 0.  8 is typically
estimated by the observed proportion.of NDs in the data set. p and a are estimated using the log values
of the detected samples where \L is estimated using the arithmetic mean of the log-detected
measurements, and a is estimated using the standard deviation of these same log values; NDs are not
included hi the calculations.  Once the parameter estimates are obtained, they are used hi the formulas
above to derive the estimated adapted delta-lognormal mean and variance.
                                   ' ) !          '       -      •     ''                  ,  •
To calculate effluent limitations and/or standards, it is also necessary to estimate upper percentiles from
the underlying data model.  Using the delta-lognormal formulation above in equation (4.1), letting Ua
represent the 100*a* percentile of random variable U, and adopting the standard notation of zs for the
s* percentile of the standard normal distribution, an arbitrary delta-lognormal percentile can be
expressed as the following:     ^
                      exp(n+o zo/1.6)   if
                            D          if
                            a za_6/1_5)  if
                                         8 +(1 -8)((log(£>) -fi)/a) ;> a
                                         8 +(1 -
(4.6)
                                              4-3

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 The daily maximum limitations are established on the basis of an estimated upper 99th percentile from
 the underlying data model, so that 0.99 would be substituted for a in the above expression. To derive
 the daily VF for the 99th percentile based on the adapted delta-lognormal model, divide U „ in the
 expression above by the previous formula for the LTA, namely U 99/E(U).


 4.2  Motivations for Modifications: to the Adapted Delta-Lognormal Model

 While the adapted delta-lognormal model has been used successfully for years by EPA hi a variety of
 settings, the model makes two key assumptions about the observed data that are not fully satisfied
 within the TEC analytical database. First, the discrete spike portion of the adapted delta-lognormal
 model is a fixed, single-valued probability mass associated (typically) with all the ND measurements.
 If all ND samples in the TEC database had roughly the same reported detection limit, this assumption
 would be adequately satisfied. However, the detection limits reported were sample-specific and,
 therefore, varied as a result of factors such as dilution.  Because of this variation in detection limits, a
 single-valued discrete spike could not adequately represent the set of ND measurements observed hi the
 TEC database and a modification to the model was considered.

 In addition, the adapted delta-lognoimal model sets all N(5 values below the detection to the Minimum
 Level of the analytical method. For example, if the Minimum Level for N-Dodecane was . 10 ug/1,
 then any NC samples reported below . 10 ug/1 were set to . 10 ug/1. There were a few instances hi the
 TEC analytical studies where an NC value was reported below the Minimum Level of the analytical
 method.
                                                  • •            )
 4.2.1 Modification of the Discrete Spike

To appropriately modify the adapted delta-lognormal model for the observed TEC database, a
modification was made to the discrete, single-valued spike representing ND measurements.  Because
ND samples have varying detection limits, the spike of the delta-lognormal model has been replaced by
a discrete distribution made up of multiple spikes.  Each spike hi this modification is associated with a
distinct detection limit observed in the TEC database. Thus, instead of assigning all NDs to a single,
fixed value, as in the adapted model, NDs can be associated with multiple values depending on how the
detection limits vary (Figure 4-3).
                                             4-4

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                                           Figure 4-3.
                            Modified Adapted Delta-lognormal Model
    Non-Detects
          V.,
          0  5101520
 In particular, because the detection limit associated with an ND sample is considered to be an upper
 bound on the true value, which could range conceivably from zero up to the detection limit, the
 modified delta-lognormal model used here assigns each ND sample to its reported detection limit;

 Once each ND has been associated with its reported detection limit, the discrete "delta" portion of the
 modified model is estimated in a way similar to the adapted delta-lognormal distribution, only now
 multiple spikes are constructed and linked to the distinct detection limits observed in the data set.  In
 the adapted model, the parameter 8 is estimated by computing the proportion of NDs.  In the modified
 model, 8 again represents the proportion of NDs, but is divided into the sum of smaller fractions, 8i5
 each representing the proportion of NDs associated with a particular and distinct detection limit. Thus
 it can be written as
                                                                                          (4.7)
If D; equals the value of the 1th smallest distinct detection limit in the data set, and let the random
variable X represent a randomly chosen ND sample, then the discrete distribution portion of the
modified delta-lognormal model can be mathematically expressed as
                                                                                          (4.8)
The mean and variance of mis discrete distribution can be calculated using the following formulas:
                                                                                          (4.9)
It is important to recognize that, while replacing the single discrete spike in the adapted delta-lognormal
distribution with a more general discrete distribution of multiple spikes increases the complexity of the  ,
model, the discrete portion with multiple spikes plays a role in limitations and standards development
identically parallel to the single spike case and offers flexibility for handling multiple observed
detection limits.                                    .
                                              4-5

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                                       CHAPTER 5
          ESTIMATION UNDER THE MODIFIED DELTA-LOGNORMAL MODEL
Once the modifications to the adapted delta-lognormal distribution are made, it is possible to fit a wide
variety of observed effluent data sets to the modified model.  Multiple detection limits for NDs can be
handled. The same basic framework can be used even if there are no ND values or censored data.

Combining the discrete portion of the model with the continuous portion, the cumulative probability
distribution of the modified delta-lognormal model can be expressed as follows, where Dn denotes the
largest distinct detection limit observed among the NDs and the first summation is taken over all those
values, Dj, that are less than u.
Pr(U<:U)  =
V

if
                                                                                      (5.1)
Again combining the discrete and continuous portions of the modified model, the expected value of the
random variable U can be derived as a weighted sum of the expected values of the discrete and
continuous lognormal portions of the distribution.  This follows because the modified delta-lognormal
random variable U can be expressed again as a combination of three other independent variables, that
is,
                               U  =
                                                                       (5.2)
where this time XD represents a random ND from the discrete portion of the model, Xc represents a
random detected measurement from the continuous lognormal portion, and !„ is an indicator variable
signaling whether any particular random measurement is detected or not.  Then the expected value and
variance of U have forms somewhat similar to the standard delta-lognormal model, namely
        E(U) =
                                        (l-8)exp(n+0.5oi2)
                                                                                      (5.3)
              Var(U)  =
                                8
                          + 8(1 -8)
                               -8)exp(2ji +O2)(exp(02) -1)
                          -expGi+0.502)
                                                                                      (5.4)
where the D; equals the individual detection limits for the NDs, the Sj are the corresponding
proportions of non-detected values with detection limit Di5 and 8 = S8;.
                                            5-1

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 5.1 Facility-Specific Estimates

 5.1.1  Estimation of Facility-Specific LTAs

 For the purposes of estimating facility-specific LTAs (equal to the expected value in equation (5.3)),
 EPA chose to divide the TEC data sets into two groups based on their size (number of samples) and the
 type of samples in the subset because the computations differ for each group. The groups were defined
 as follows:

    Group 1: Less than 2 NC samples or less than 4 total samples.

    Group 2: Two or more NC samples and 4 or more total samples.

 For Group 1, the LTAs were calculated as the arithmetic average of the samples, since the sample sizes
 for either the discrete portion or the continuous lognormal portion of the data were too small to allow
 distributional assumptions to be made. Specifically, Group 1 contained all data subsets with all NDs or
 only one detect.  Sample-specific  detection limits were substituted as the values associated with non-
 detectable samples. Group 1 also contained all data for the Truck/Chemical  subcategory since no more
 than three samples were collected for each facility.

 For Group 2, the LTAs were calculated using the procedures outlined in the  preceding section using
 equation (5.3) and the Maximum  Likelihood Estimates (MLEs) for ft and o.

 5.1.2 Estimation of Facility-Specific VFs

 After determining estimated LTA values for each pollutant, facility, and option combination, EPA
 developed 1-day VFs for all regulated pollutants, and 4-day VFs for the Conventional pollutants, which
 include Hexane Extractable Material, BOD 5-day, Total Suspended Solids, and Total Organic Carbon.

 The data were divided into the same two computation groups presented hi Section 5.1.1 for purposes of
 estimating VFs. For Group 1, upper percentiles and VFs could not be computed using the modified
 delta-lognormal methodology. Several data subsets belong hi Group 1, and therefore have missing 95th
 and 99th percentiles and VFs.  For Group 2, the estimates of the parameters for the modified delta-
 lognormal distribution of the data  were calculated using maximum likelihood estimation hi the log-
 domain.  Upper percentiles and VFs were calculated using these estimated parameters. Calculation of
 these VFs are described hi Section 5.3.
5.1.2.1 Estimation of Facility-Specific 1-day VFs

The 1-day VFs are a function of the LTA, E(U), and the 99th percentile. An iterative approach was
used in finding the 99th percentile of each data subset using the modified delta-lognormal methodology
by first defining D0=0, 80=0, and Dk+1  = <=° as boundary conditions, where D; equals the i* smallest
detection limit, and 8. is the associated proportion of NDs at the i* detection limit.  A cumulative
distribution function, p, for each data subset was computed as a step function ranging from 0 to  1. The
general form, for a given value c, is
                                            5-2

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                       '=0
Dn ± c < Dm+l,
    "
                                                                                         (5.5)
where <3> is the standard normal cumulative distribution function. The following steps were completed
to compute the estimated 99th percentile of each data subset:

  1.  k values of p at c=Dm, m=l,:.:k were computed and labeled pm.

  2.  The smallest value of m, such that pm z 0.99, was determined and labeled as PJ.  If no such m
     existed, steps 3 and 4 were skipped and step 5 was computed instead.                    ,

  3.  Computed p* = PJ - 8j.

  4.  If p*  < 0.99, then P99 = DJ5
     else if p* ;> 0.99, then
                             P99 =exp
                                                0-99 -
                                                                                        (5.6)
  5.  If no such m exists, such that pm k 0.99 (m=l,...k), then

                                                  0.99 -6]
                                P99 =exp
                                               s-i
                                                   (i -S)
                                      (5.7)
The daily VF, VF1 > was then calculated as

                                 P99
                         VF1 = ±2?-
                                                                                       (5.8)
5.1.2.2 Estimation of Facility-Specific 4-day VFs

Since EPA is assuming for costing purposes that the Conventional pollutants will be monitored weekly
(approximately four times a month), EPA calculated a VF for monthly averages based on the
distribution of 4-day averages.  In order to calculate the 4-day VF, the assumption was made that the
approximating distribution of U4, the sample mean for a random sample of four independent
concentration values, also is derived from this modified delta-lognormal distribution,.with the same
mean as the distribution of the concentration values. The mean of this distribution of 4-day averages is

                          £(£/,) • = dtE(X4)D + -(1-84)E(X4)C-                               (5.9)

where (X4)D denotes the mean of the discrete portion of the distribution of the average of four
independent concentration values (i.e., when all observations are not detected), and (X4)c denotes the
                                            .5-3

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 mean of the continuous lognormal portion of the distribution.

 First, it is assumed that the probability of detection (6) on each of the four days is independent of that
 on the other days,  and are therefore not correlated such that S4 = 84.  Also, since

                                         E(X4)D  = E(XD)
 then
                             = 64E-     + (l-S4)eXpGi4+0.5o24)
                                                                    (5.10)
 and since E(tJ4) = E(U), then
                            4 = log
                                           -84)
                              -0
                                                                                          (5.11)
 The expression for o% was derived from the following relationship

                                                   + S4(l -
Shice
                                         E(X4)D=E(XD),   and
                                                                   (5.12)
                                                                   (5.13)
then
          Fizr(E/4) =
                                                                   (5.14)
This further sunplifies to
                        t   k
-84)
                                           4 +0.50^
                                                                                          (5.15)
and furthermore,
                                              5-4

-------
exp(o24)-l = 1
                            it   k
                                              -82(1 -S4) E 8,0, -SexpOi, +0.50%)
                                      (l-84)exp(2jl4+024)
                                                                                         (5.16)
Then, from (5.10) above,
 exp(n4+0.5a24)=
                                           (1-S4)
                                        -,    since E(U4) =E(U)
                                                                                         (5.17)
and letting
                                       then,  exp(fi4+0.5024)  = -^.
                                       ,      -                    -4
                                                                           (5.18)
Furthermore,
      o24=log
1 +
                               it   it
                                      4
                                                        (1
(5.19)
Since Var(04) = Var(U)/4, then, by rearranging terms,
     o24 = log
                                                4T!2
                                                               n
                                                                                         (5.20)
Thus, estimates of #, and o4 were derived by using estimates of 8j,...8k (sample proportion of NDs at
observed detection limits D^.-.D^, /* (MLE of logged values), and a2 (MLE logvariance with sample
bias adjustment) in the equations above.

In finding the estimated 95th percentile of the average of four observations, four NDs, not all at the
same detection limit, an average can be generated that is not necessarily equal to D15 D2,..., or Dk.
Consequently, more than k discrete points exist in the distribution Of the 4-day averages.  For example,
the average of four NDs at k=2 detection limits are at the following discrete points with the associated
                                             .5-5

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probabilities:
:i
2
3
4
5
Dl
(3D, +£>2)/4
(2Dj+2D2)/4
(D1+3D2)/4
D
V
48j382
66i2622
46i623
8,4
In general, when all four observations are hot detected, and when k detection limits exist, the
multinomial distribution can be used to determine associated probabilities; that is,
                         Pr
u^
                   4!
                        •id"'.
                                                                                        (5.21)
The number of possible discrete points, k*, for k=l,2,3,4, and 5 are given below:
                             k      k!
                             11
                             2      5
                             3       15
                             4      35
                             5      70

To find the estimated 95th percentile of the distribution of the average of four observations, the same
basic steps (described in Section 5.1.2.1) as used for the 99th percentile of the distribution of daily
observations were followed, with the following changes:

  1. Change Pgg to P9S, and 0.99 to 0.95.
  2. Change Dm to Dm*, the weighted averages of the detection limits.
  3. Change 8j to 8.*.
  4. Change k to k*, the number of possible discrete points based on k detection limits.
  5. Change the estimates of 8, /t, and a to estimates of 84,  /*4, and o4, respectively.

Then, the estimate of the 95th percentile 4-day mean VF is:
VF4 =
        PQ5
       — ^-,
       E(U)
                                                      -
                                             since  E(U4) = E(U).
Appendices C.I - C.7 display facility-level LTAs, 1-day VFs, and 4-day VFs by subcategory, option,
and pollutant.
                                             5-6

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 5.2 Pollutant-Specific Estimates

 5.2.1  Estimationof Pollutant-Specific LTAs

 After estimating the facility-specific LTAs for each pollutant and option by subcategory, as described in
 section 5.1.1, pollutant-specific LTAs were calculated.  Pollutant-specific LTAs provide one number
 for all facilities within a subcategory and option.  Within each subcategory and option combination, the
 pollutant-specific LTAs were calculated as the median of the facility-specific LTAs for that pollutant.
 The median is the midpoint of the values ordered (i.e., ranked) from smallest to largest. If there is an
, odd number of values (with n=number of values), then the value of the (n+1)/2 ordered observation is
 the median. If there are an even number of values, then the two values of the n/2 and [(n/2)+1]
 ordered observations are arithmetically averaged to obtain the median value. Since data for all
 subcategory and option combinations exist for only one or two facilities, the median pollutant-specific
 LTAs equal the mean pollutant-specific LTAs.

 If, for example, the facility-specific LTAs for SGT-HEM in Option I of the Rail/Chemical Indirect
, subcategory are:                                                   •
 Facility
 A
 B
 C
T7TA
25.0 mg/1
45.0 mg/1
29.0 mg/1
 then ordered values are:
 Order
 1
 2
 3
Facility
A
C
B
LTA
25.0 mg/l
29.0 mg/1
45.0mg/l
 and the pollutant-specific LTA for Option I is the median of the ordered values, or 29.0 mg/1.  Since
 there were a maximum of two facilities in each subcategory and option combination, consider the above
 example but exclude Facility C.   Then the pollutant-specific LTA is calculated by averaging the
 concentrations for Facility A and Facility B, or (25+45)/2 = 35 mg/1.                           j  •

 5.2.2  Estimation of Pollutant-Specific VFs

 5.2.2.1  Estimation of Pollutant-Specific 1-day VFs

 After the facility-specific VFs were estimated for each pollutant and option by subcategory, as
 described in section 5.1.2.1, the pollutant-specific VFs were calculated.  Pollutant-specific VFs provide
 an estimate of the average variability for the pollutant across all facilities within a subcategory and
 option. The pollutant-specific 1-day VF was the mean of the facility-specific daily VF for that pollutant
 in the subcategory and option combination.              .

 5.2.2.2  Estimation of Pollutant-Specific 4-day VFs

 After the facility-specific 4-day VFs were estimated for each pollutant and option by subcategory, as
 described in section 5.1.2.2, the pollutant-specific 4-day VF was calculated.  The pollutant-specific 4-
 day VF was the mean of the facility-specific 4-day VF for that pollutant in the subcategory and option
                                              5-7

-------
 combination.  For pollutants not belonging to a group or fraction (see Section 5.3), the pollutant-
 specific 1-day and 4-day VFs were used to calculate 1-day and 4-day limitations, as discussed in
 Chapter 6.


 5.3 Estimation of Group-Level and Fraction-Level VFs

 This section describes the estimation of group-level and fraction-level VFs by subcategory and option.
 Each group contains pollutants that are chemically similar and, therefore, have similar treatability by
 the treatment option in consideration.  These groups were further aggregated by the type of fraction
 used in the chemical analysis.  Appendix D shows the assignment of pollutants to groups and fractions.
 Chapter 6 describes how the group-level and fraction-level VFs were used to calculate limitations.  For
 some pollutants, including all pollutants in the Truck/Chemical Direct and Indirect subcategories, VFs
 were transferred because there were not enough total samples collected to assume a delta-lognormal
 distribution and estimate VFs at the group-level or fraction-level (see section 5.4).

 5.3.1 Estimation of Group-Level 1-day VFs

 After calculating pollutant-specific 1-day VFs, as described hi sections 5.2.2.1 and 5.2.2.2, the group-
 level 1-day VFs were calculated.  Group-level 1-day VFs were the median of the pollutant-specific 1-
 day VFs. If, for example, in Rail/Chemical Indirect Option I the pollutant-specific  1-day VFs in the N-
 Paraffins group are:
  Pollutant
  N-Dodecane
  N-Eicosane
  N-Tetracosane
  N-Octadecane
1-day VF
  6.4
  5.3
  2.1
  8.2
then the ordered values are:
  Order
  1
  2
  3
  4
Eollulanl
N-Tetracosane
N-Eicosane
N-Dodecane
N-Octadecane
1-day VF
  2.1
  5.3
  6.4
  8.2
Then the group-level VF is the median of the ordered values (i.e. the average of the 2nd and 3rd
ordered values): (5.3+6.4)72 = 5.85.

These group-level VFs were used in the calculation of limitations. Group-level 4-day VFs were not
calculated since the proposed monthly limitations only cover Conventional pollutants, which do not
belong to a group.  Table 5-1 displays the group-level 1-day VFs by subcategory and technology
option.
                                             5-8

-------
                                          Table 5-1.
                      Group-level 1-day VFs by Subcategory and Option
"*•"•<. • _, , M ""X. WTS " "a* !%,
. •* ^Subcategorx /' ^> A „
Barge/Chemical & Petroleum Direct
Barge/Chemical & Petroleum Indirect
Rail/Chemical Direct
Rail/Chemical Indirect
" f"/ ~
'-iQptkffif
I
n
H
i
i
i
i
m
m
m
i
i
i
ni
in
m
; GiWp ;, -
Metals
PAHS
Metals
N-Paraffins
PAHS
Metals
Ketones, Aliphatic I
PAHS
Metals
Ketones, Aliphatic I
N-Paraffins
PAHS
Metals
N-Paraffins
PAHS
Ketones. Aliphatic I
l-day,TFfttg/l)^
2.84
11.20
_^ (
7.44
3.28
5.39
4.48
1.10
5.39
2.76
2.00
7.56
5.39
4.48
8.37
5.39
2.90
For some group and technology option combinations, there were not enough non-censored data
available to calculate 1-day VFs.  In these cases, fraction-level 1-day VFs were calculated as explained
in the next section. Group-level VFs and fraction-level VFs could not be calculated for pollutants in
the Truck/Chemical Direct & Indirect subcategories, due to insufficient data at the facility level. In the
Food Grade Direct subcategory all regulated pollutants are Conventional and are not grouped,
therefore-no group-level VFs exist.
                                              /
5.3.2 Estimation of Fraction-Level 1-day VFs

Fraction-level 1-day VFs were the median of the group-level 1-day VFs for the groups that used the
fraction: in the chemical analysis.  For example, suppose that the group-level 1-day VFs for the fraction
Base-Neutrals in Rail/Chemical Indirect Oiption I are:     ,                                ,
  Group
  N-Paraffins
  PAHS
  Phthalates
Uday VF
 5.9
 6.5
 3.0
                                             5-9

-------
 then the ordered values are:
   Order
   1
   2
   3
Group
Phthalates
N-Paraffins
Phthalates
1-day VF
  3..0
  5.9
  6.5
 Then the fraction-level 1-day VF is the median of the ordered values, or 5.9.

 Fraction-level VFs were used in the calculation of limitations if the group-level VFs could not be
 estimated.  Table 5-2 presents daily fraction-level VFs by subcategory and technology option.

                                           Table 5-2.
                      Fraction-level 1-day VFs by Subcategory and Option
Subcategory
Barge/Chemical & Petroleum Direct
Barge/Chemical & Petroleum Indirect
Rail/Chemical Direct
Rail/Chemical Indirect
Option
I
n
n
i
i
i
m
m
m
i
i
m
m
Fraction.
Metals
Base-Neutrals
Metal
Base-Neutrals
Metal
Volatile
Base-Neutrals
Metal
Volatile
Base-Neutrals
Metal
Base-Neutrals
Volatile
2-day VF (ug/1)
2.84
11.20
7.44
4.33
4.48
1.10
5.39
• 2.76
2.00
6.47
4.48
6.88
2 90
For some fraction and technology option combinations, there were not enough non-censored data
available to calculate fraction-level VFs. In these cases, 1-day VFs were transferred from other
technology options or subcategofies, as described hi Section 5.4.

                               *                 /                   •
5.4 Transfer of 1-day and 4-day VFs

If, for a pollutant hi a selected subcategory and technology option, group-level or fraction-level 1-day
VFs could not be calculated, it was necessary to transfer 1-day VFs from other technology options or
subcategories. Table 5-3 presents the group (or pollutant if no group exists), subcategory, and
                                             5-10

-------
 technology options with missing VFs and the technology option and subcategory from which the 1-day
 VFs were transferred.

                                          Table 5-3.
               Subcategory, Option, and Group Combinations with VF Transfers
- ; " Sub&tegorJ; *^'<
Rail/Chemical Indirect
Rail/Chemical Direct
Barge/Chemical &
Petroleum Indirect
Barge/Chemical &
Petroleum Direct
Food Grade Direct
Group/Pollutant
X, f *< t f *
N-Paraffins*
N-Paraffins*
BOD 5-day
SGT-HEM
N-Paraffins*
Aromatics
Phthalates
PAHS
SGT-HEM
Hexane
Extractable
Material
v Option/*
I
I
I, HI
n
i
i
i
i
i
n
\ f '- ^ ~ TraiilfeiTed Brain ( ^ f; >;;y
Rail/Chemical Indirect Option HI
Rail/Chemical Indirect Option HI
Barge/Chemical & Petroleum Direct
Option I
Barge/Chemical & Petroleum Indirect
Option IV.
Barge/Chemical & Petroleum Direct
Option III
Barge/Chemical & Petroleum Direct
Option m
Barge/Chemical & Petroleum Direct
Option IH,
Barge/Chemical & Petroleum Direct
Option III
Barge/Chemical & Petroleum Direct
Option in
Food Grade Direct Option I
*N-Parafnns transferred from Option HI to Option I due to improperly operating organo-clay unit used in NSPS for pollutants
in this group.                                                   •  .

In several subcategories, variability factors were transferred from options that are not regulated.  These
include: Barge/Chemical & Petroleum Indirect Option IV which consists of Flow Reduction, Oil/Water
Separation, and Dissolved Air Flotation; Barge/Chemical & Petroleum Direct Option in which consists
of: Flow Reduction, Oil/Water Separation, and Dissolved Air Flotation; and Food Grade Direct Option
I which consists of Flow Reduction, Oil/Water Separation,  Equalization, and Sludge Dewatering.

Truck/Chemical Indirect

Since sampling for the Truck/Chemical Indirect subcategory was conducted for only three days, there
were not enough data to calculate 1-day VFs at the pollutant-level, group-level, or fraction-level. As
such, VFs for Truck/Chemical Indirect were transferred from Rail/Chemical Indirect.  The following
steps were followed to transfer 1-day VFs from Rail/Chemical Indirect to Truck/Chemical Indirect.
                                             5-11

-------
1.   The group-level VFs from Rail/Chemical Indkect Option HI regulated pollutants were transferred
     to Truck/Chemical Indirect PSES and PSNS Option II.

2.   If the group was not regulated in Rail/Chemical Indirect Option III, the Option HI fraction-level
     VFs were transferred to Truck/Chemical Indkect PSES and PSNS Option II.

3.   If neither the group nor the fraction is regulated in Rail/Chemical Indirect Option ffl, all
     pollutants in the list of Rail/Chemical Indkect POCs for the groups (and then fraction if group is
     not available) with missing VFs were added to the list of pollutants.  Then, the group-level VFs
     for the Rail/Chemical Option HI were recalculated and transferred to Track/Chemical Indirect
     PSES and PSNS Option H.

4.   If the VF was still missing, as in the case of the Metals, the VFs were transferred from
    • Rail/Chemical Indkect Option n. Rail/Chemical Indkect Option II consists of the following:
     Flow Reduction, Oil/Water Separation, Equalization, Dissolved Ak Flotation (with Flocculation
     and pH Adjustment), and Sludge Dewaterihg.

Truck/Chemical Direct

As in the Truck/Chemical Indkect subcategory, sampling  was conducted for three days. Therefore,  1-
day VFs were transferred from Rail/Chemical Dkect Option II in the following order:

1.   The group-level VFs from Rail/Chemical Dkect Option II regulated pollutants  were transferred
     to Truck/Chemical Dkect BPT, BCT, BAT, and NSPS Option H. Rail/Chemical Dkect Option
     n consists of the following: Flow Reduction, Oil/Water Separation, Equalization, Dissolved Ak
     Flotation (with Flocculation amd pH Adjustment), Biological Treatment, and Sludge Dewatering.

2.   If the group was not regulated in Rail/Chemical Dkect Option H, the Option n fraction-level VFs
     were transferred to Track/Chemical Dkect BPT and BAT Option II.

3.   If neither the group nor the fraction was regulated in Rail/Chemical Direct Option II, all
     pollutants in the list of Rail/Chemical Dkect POCs for the groups (and then fraction if group is
     not available) with missing VFs were added to the list of pollutants.  The group-level VFs for
     Rail/Chemical Dkect Option n were recalculated and transferred to Truck/Chemical Dkect BPT,
     BCT, BAT, and NSPS Option E.     .

4.   For BOD 5-day, the VFs were transferred from Barge/Chemical & Petroleum Dkect BPT, BAT,
     and NSPS Option I.
                                           5-12

-------
                                         CHAPTER 6
                      DERIVATION OF THE PROPOSED LIMITATIONS

The proposed daily and monthly maximum limitations .are presented in grams/tank  These limitations
are referred to as mass based limitations.  EPA is proposing mass based effluent guideline limitations
and standards in order to prevent dilution of effluent wastewater and to contribute to water
conservation.  Mass based limits were calculated as the product of the pollutant-specific concentration
based limitation, median flow per tank type cleaned, and a conversion factor. This chapter describes
the methods used to derive the proposed daily and monthly mass based limitations.
                                  H •                    -         .         '\ ,       '

6.1 Steps Used to Derive Concentration Based Limitations

The derivation of the concentration based daily maximum limitations used the pollutant-specific LTAs
and the group-level daily VFs.  Daily maximum limitations for Conventionals were based on the
pollutant-specific LTA and 1-day VF.  Monthly limitations were calculated for the Conventional
pollutants which do not belong to a chemical group.  Thus, the derivation-of the concentration based
maximum for monthly average limitations used the pollutant-specific LTAs and the pollutant-specific 4-
day VFs.       . '              •                            ''....

Listed below are the steps that were followed in order to derive the concentration based limitations:
Appendices E.I - E.7 provide, by subcategory, listings of the concentration based pollutant-level
limitations with the LTAs and VFs used to derive the final limitations. Also included in these listings is
a variable labeled 'V.F. Type,' which indicates whether group-level, fraction-level,  or transferred VFs
were used.

Step 1:   The facility-specific LTAs and 1-day and 4-day VFs were calculated for all facilities.
         Calculation of VFs was only performed when the facility had four or more observations with
         two or more distinct detected values (i.e., Group 2).

Step 2:   For each option hi the subcategory, the median of the facility-specific LTAs and the mean of
         the facility-specific  1-day and 4-day VFs were calculated to provide pollutant-specific LTAs
         and 1-day and 4-day VFs.

Step 3:   The group-level 1-day VF was calculated using the median of the pollutant-specific 1-day
         VFs for the pollutants within each group.

Step 4:   If the group-level 1-day VF could not be calculated, the fraction level 1-day VF was
         calculated as the median of the group-level 1-day VF.  If the fraction-level 1-day VF  could
         not be calculated, a VF transfer (see Section 5.4) was conducted at the group-level or the
         fraction-level, if necessary.

Step 5:   In most cases, the daily limitations for a pollutant were calculated using the product of the
         pollutant-specific LTA and the group-level 1-day VFs.  If the group level 1-day VF could not
         be estimated or transferred, then the product of the pollutant-specific LTA and the fraction-
         level 1-day VF was used in calculating the limitation. If the pollutant did not belong to a
         group or fraction, the daily limitation was calculated using the product of the pollutant-
         specific LTA and the pollutant-specific 1-day VF.  Monthly limitations were calculated using
   .      the product of the pollutant-specific LTA and the pollutant-specific 4-day VF.      ,
                                              6-1

-------
6.2 Estimated Median Flow per Tank Type Cleaned

The estimated median flow per tank type cleaned was calculated for the following subcategories:
Rail/Chemical, Truck/Chemical, Barge/Chemical, Truck/Food, Rail/Food, and Barge/Food. "These
estimates were based on flow values reported by tank type cleaned, which were extracted from the
TECI Detailed Questionnaire (DQ) distributed to sampled facilities in 1994.  Flow was defined as the
gallons of TEC wastewater generated per day for each wastewater stream. Table 6-1 displays the
estimated median flow per tank type; cleaned for each subcategory.

                                          Table 6-1.
                        Estimated Median Flow per Tank Type Cleaned
Subcategoiy
Rail/Chemical
Barge/Chemical
Truck/Chemical
Truck/Food
Rail Tank/Food
Barge/Food
Type of Tank
Cleaned*
Rail
Barge
Truck
Truck
Rail
Barge
Estimated Median
Value (gals/tank)
2091
4857
605
790
4500
4500
Section 6.2.1 describes the methods used to derive the estimated median flow per tank type cleaned for
a subcategory.
6.2.1 Statistical Methods for Estimating Median Flow Values and Confidence Intervals About the
     Estimates

The first step hi calculating the estimated median flow value was to determine the value of the 50th
percentile.  A percentile is me value in an ordered set of measurements such that p% of the measures
lie below that value.  For example, consider a facility that has cleaned the following tanks in one day:
Tank
Flow
(gal/tank)
1
500
2
3500
3
200'
4
100
5
1400
6
1600
7
3200
8
4000
9
1200
10
5300
If the tanks (obs) are ordered from ihe smallest to largest value, the result would be as follows:
Obs
Flow
(gal/tank)
1
100
2
200
3
500'
4
1200
5
1400
6
1600
7
3200
8
3500
9
4000
10
5300
With the ordered set of observations, the next step is to estimate the cumulative probability such that

                                             6-2

-------
With the ordered set of observations, the next step is to estimate the cumulative probability such that
the probability of observing a particular flow less than a given flow value using the empirical data is
known, The following table displays the calculated probability (prob) of observing each flow and the
cumulative probability (cum) of observing each flow.
Obs
Flow
(gal/tank)
Prob
Cum
1
100
0.1
.1
2
200
0.1
.2
3
500
0.1
.3
4
1200
, 0.1
.4
5
1400
0.1
•5
6
1600
0.1
.6
7 ;
3200
0.1
.7
8
3500
0.1
.8
9
4000
0.1
.9
10
5300
0.1
i
With this table, it is possible to determine the value such that p% of the measures lie below that value.
For example, the median or 50th percentile fdr this data set is estimated by averaging flow for
observations 5 and 6 which gives (1400+1600)/2 = 1500 gal/tank. Note.that the median is not the
value of me flow where the cumulative percent equals .5, since the cumulative probability of observing
a flow value is equal to but not greater than .5.  The first instance where 50% of the flow values lie
below a particular flow value is somewhere between the flow values of observations 5 and 6, as
presented in the table above. In this example each flow value has an equal probability of being selected
(prob=.l), and therefore, averaging observations 5 and 6 represents the estimated median flow value.

Based on responses to the TECIDQ, percentiles were calculated for flow values.  As discussed in the
Final Transportation Equipment Cleaning Industry Detailed Questionnaire Sample Design Report
(DCN # Til, 110), only a sample of facilities received a DQ and each was assigned a survey weight
(i.e.  the facility used in the example above could represent other facilities in addition to itself).
Consequently, each flow value from the DQ does not represent an equal percent of the population flow
values, but rather a weighted percent.  From these weighted flow values, percentiles were determined.

Changing the example presented above, consider that the flows were collected under a design-based
probability sampling scheme.  Under this sampling scheme, consider that these 10 observations were
selected from a population of 200 facilities divided into 4 groups, or strata (e.g. facilities that are
operationally similar may be grouped together or stratified). The sizes of each group are 75 (I), 60
(H),  40  (III),  and 25 (IV) with 3, 3, 2, and 2 sampled within each group, respectively.  For each group,
the weights are calculated as the number of observations within the group divided by the number
sampled. Thus,  the weights for groups I through IV are 25 (or 75/3), 20,  20, and 12.5, respectively.
Using the weights, the probability of observing each flow is calculated as the weight divided by the
total number within the population (e.g. group II is 20 (weight) divided by 200 (the total population of
facilities), which equals 0.1).  The following table depicts the ordered observations with the group
(group), design weight (weight), probability (prob), and cumulative probabilities (cum) associated with
each value.                                          •'...-•                  .         «  .
                                             6-3

-------
Obs
Flow
(gal/tank)
Group
Weight
Prob
Cum
1
100
n
20
0.1
0.1
2
200
I
25
0.125
0.225
3
500
I
25
0.125
0.35
4
1200
n
20
0.1
0.45
5
1400
in
20
0.1
0.55
6
1600
n
20
0.1
0.65
7
3200
I
25
0.125
0.775
8
3500
IV
12.5
.0625
.8375
9
4000
in
20
0.1
.9375
10
5300
IV
12.5
.0625
1.0
 Notice that the 50th percentile (Pso), is now a value between 1200 and 1400. Therefore, in order to
 determine the flow value at the 50th percentile, it is necessary to interpolate based on the probability of
 observing each value. Using this example, define the lower value as 1200 (Value^J and the upper
 value as 1400 (ValueHigh) such that the percentile of interest is captured by the cumulative probabilities
 of each value.  Also, define PLow as the cumulative probability associated with observing the lower
 value, PHi|h as the probability associated with observing the higher value, and X as the percentile of
 interest. Then the percentile is calculated as follows:

                 Px = ValueLow +  (ValueHigh - Value^) * [(X - P^ )/(PHigh -P^ )]

 Using the median or 50th percentile  as an example, the following would result.

          Pso  = 1200 +(1400-1200) *  ('S° ~'45)  =  1200+200*—  =  1200+100 =  1300
                                     I (.55-.45) I                .1
Similarly, for the 90th percentile, the following would result.
     P9Q = 3500 +(4000 -3500)*
 (.90-.8375)

(.9375-.8375)
= 3500 +500 *
.0625

  .1
= 3500+312.5  = 3812.5
As demonstrated, in order to calculate percentiles for a sample based on a weighted design, it is
essential to consider that each value has a design-based probability of occurring.  Point estimates of the
median (50th), 75th, 90th, 95th, and 99th percentiles of the flow per tank type cleaned were calculated by
subcategory and type of tank cleaned using the methods described above.  Section 6.2.2 provides the
mathematical formulas used to calculate these percentile estimates.

6.2.2 Percentile Estimates

Computationally, the flow values rejported by each facility (yhij) are arranged in ascending order across
all values of the indices.  The survey weights associated with the arranged yhij are summed until the first
instance when the value of p is exceeded.  The mathematical formulation is presented below.

Denote the p* percentile of the distribution F as 6p. Define 6p as

                                       0p=inf{F(Y)< p}.
                                              6-4

-------
The cumulative distribution, F(Y), is estimated as

with
                                              w.
                                    w
                                      hi
                                            12  5
                                           ££»*
 where   whj   = survey weight for subcategory h, tank type j
         yhij   = flow from 1th facility in subcategory h, tank type j

         ahij   =1 if yhij < y; 0 otherwise.     .        ,
Thus, the p* percentile is estimated as
                                                     1.
Appendix F presents the 75th, 90th, 95th, and 99th percentile estimates for flow per tank type cleaned.
6.3 Unit Conversion Factor
                                                                                      ('

In order to convert mass based limitations to grams/tank, a conversion factor was applied.  If flie

concentration based limitations were presented in ing/1, the conversion factor, k, was
                           k =
                                   1,000;
 g
                                 264.17gal){l,OOQmgJ
        = .0037854.
For concentration based limitations reported in ug/1, the conversion factor, k, was
                            k =
                                   1,0001
J—\ =
                                  264.17gal)( lQ6ug/l)
.0000038.
                                              6-5

-------
 6.4 Mass Based Limitations

 6.4.1  Daily Mass Based Limitations
                                                                                       v
 For each subcategory, pollutant-specific daily maximum mass based limitations were calculated as the
 product of the daily maximum concentration based limitation, the estimated median flow per tank
 cleaned, and the units conversion factor (k). For example, the mass based daily maximum limitation
 for Pyrene in Rail/Chemical Direct NSPS (Option HI) was calculated as follows:  As indicated in Table
 6-1, the estimated median flow per tank cleaned for Rail/Chemical Direct is 2,091 gallons per tank.
 The daily maximum limitation for Pyrene (see Appendix E.2) is 85.5 ug/1. Thus, the mass based daily
 maximum concentration is 85.5 ug/1 * 2,091 gallons/tank * .0000038 = 0.68 grams/tank. Appendices
 G.I - G.9 present the proposed daily maximum limitations, concentration based limitations, flow per
 tank type cleaned, and conversion units for the regulated pollutants by subcategory and the selected
 technology option.

 6.4.2  4-day Mass Based Limitations

 For the Conventional pollutants, monthly, or 4-day, mass based limitations were calculated as the
 product of the 4-day maximum concentration based limitation, the estimated median flow per tank type
 cleaned and the units conversion factor, k. For the remaining pollutants, the monthly mass based
 limitations were assumed to be the same as the daily mass based limitations, since EPA assumed it was
 reasonable to sample once per month for these pollutants rather than four times per month. Appendices
 G.I - G.9 present the proposed monthly maximum limitations, concentration based limitations,  flow per
 tank type cleaned, and conversionjmits for the regulated pollutants by subcategory and the selected
 technology option.

6.4.3 Transfer of Mass Based Limitations

For two pollutants, it was necessary to transfer mass based limitations.  The mass based daily limitation
for Chemical Oxygen Demand (COD) in the Barge/Chemical Indirect subcategory PSNS (Option n)
was transferred from Barge/Chemical Direct BAT and NSPS (Option I). The daily limitation for COD
was also transferred from Truck/Chemical Direct to Truck/Chemical Indirect and Rail/Chemical Direct
to Rail/Chemical Indirect.  In Rail/Chemical Direct BPT and BCT (Option I), the daily and monthly
maximum limitations for Total Suspended Solids (TSS) were transferred from Rail/Chemical Direct
NSPS (Option HI). Tables 6-2 through 6-10 present the mass based limitations for each subcategory.
                                            6-6

-------
                        Table 6-2.
Truck/Chemical Direct Subcategory: BPT, BCT, BAT, and NSPS
              Proposed Mass Based Limitations
„ -- tf. :-~ ^\^;^;^\v^/^^m^/^ ^ - "^ ^w*;, „ ' "*•>•
*S *<•>** x'* ^ EU v i j. H * ?* X*1 i ^ « .,""**?£* v* »* * >> v «S *\ <• \ ti ^ *•" v -i ^J-^^^S f
"ML ^ -iar,*Sx,^- ^»;>
:', ^P0Hutan£ '"
*• , '- " ^ ~i ,
-•* I- 5 3 f ~
^ ^ ^ < "^ „, .
*^^ ^*
BOD5
TSS
HEM
Chromium
Zinc
COD
Bis (2-ethylhexyl)
pthalate
di-N-octyl phthaiate
N-Dodecane
N-Hexadecane
Styrene
1 ,2-dichlorobenzene
'Jii^hn: r-?fe
^toaftf"-^1'
Maxinnim
145.00
28LOO
25.30
0.16
0.09
3,760.00
0.12
0.12
0.12
0.12
0.20
0.12
* « [ j, ^ t ? j. »
Monthly
^erage
' 67.60
115.00
16.10
0.16
0.09
,3,760.00
0.12
0.12
0.12
. 0.12
0.20
0.12
".: > icitV,,/
"' Daily /
Maxknum
^^ ?
145.00
281.00
25.30
N/A
N/A
N/A
•' N/A
N/A
N/A
N/A
N/A
N/A
•» i "^^j-S"*^
^ontMy^
Average
67.60
115.00
16.10
N/A
N/A
N/A
N/A
.N/A
N/A
N/A
N/A
N/A
-BAfs^
£ My v"(
Maximuni
N/A
N/A
N/A
- 0.16
0.09
3,760.00
0.12 .
0.12
0.12
0.12
0.20
0.12
. * l^$8fcjj('1 f{*
^ BSa.yT ^
Maxiimim
< «s, ~,
145.00
281.00
25.30
0.16
0.09
3,760.00
0.12
0.12
0.12
0.12
0.20
0.12
Monthly
Average
67.60
115.00
16.10
0.16
0.09
3,760.00
0.12
0.12
0.12
0.12
0.20
0.12
                           6-7

-------
                    Table 6-3.
Truck/Chemical Indirect Subcategory:  PSES and PSNS
          Proposed Mass Based Limitations
„.,,;: , ' Grams/Tank^ 5 ;s ,T ^~7 ",""*> .*"' % ~ «V ^
Pollutant
Chromium
Zinc
COD
Bis (2-ethylhexyl) pthalate
di-N-octyl phthalate
N-Dodecane
N-Hexadecane
Styrene
1 ,2-dichlorobenzene
PSES ,
Daly
Maximum
0.20
0.12
3,760.00
0.23
0.15
0.19
0.19
0.40
0.15
Monthly
Average
0.20
0.12
3,760.00
0.23
0.15
0.19
0.19
0.40
0.15
PSNS
Daily
- Maximum
0.20
0.12
3,760.00
0.23
0.15
0.19
0.19
0.40
0.15
"Monthly
Average
0.20
0.12
3,760.00
0.23
0.15
0.19
0.19
0.40
0.15
                      6-8

-------
                                  Table 6-4.
          Rail/Chemical Direct Subcategory: BPT, BCT, BAT, and NSPS
                        Proposed Mass Based Limitations
. L" i" " ^ <# f VM> ^ . *- J T *&**$>&? rt ; ^: f f* : r !V V i- ,v J
•_ -'"Potlu^nt J-"
A S" A ^  D^tty>, ^*-
Maxiinuin
j » jsr '
3,840.00
338.00
470.00
N/A
N/A
N/A
N/A
N/A
N/A
.N/A
N/A
^Rfontbly
^-ATCEage^
1,790.00
141.00
. 286.00
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
< BAT5

f %£** ^ -*,** &
N/A
N/A
N/A '
42,200.00
0.63
0.43
0.43
2.20
0.68
0.74
1.96
^ ^ ^ ?K-
Maximum
3,840.00
338.00
130.00
42,200.00
0.43
0.43
0.43
2.20
0.68
0.74
1.96
% Monthly j
1,790.00
141.00
83.00
42,200,00
0.43
0.43
0.43
2.20
0.68
0.74
1.96
                                  Table 6-5.
               Rail/Chemical Indirect Subcategory: PSES and PSNS
                        Proposed Mass Based Limitations
SGT-HEM
COD
N-Hexadecane
N-Dodecane
N-Tetradecane
Fluoranthene
   942.00
42,000.00
     2.56
     6.57
     3.98
     0.60
     942.00
,  ,42,000.00
       2.56
       6.57
       3.98
       0.60
   207.00
42,000.00
     2.56
     0.66
     0.66
     0.60
   207.00
42,000.00
     2.56
     0.66
     0.66
     0.60
                                     6-9

-------
                              Table 6-6.
Barge/Chemical & Petroleum Direct Subcategory: BPT, BCT, BAT, and NSPS
                    Proposed Mass Based Limitations
Grams/Tank \ p %/ ' .. "'- 1
Pollutant,
BOD5
TSS
HEM
COD
Cadmium
Chromium
Copper
Lead
Nickel
Zinc
1-
Bis (2-ethylhexyl)
Di-N-Octyl Phthalate
N-Decane
N-Docesane
N-Dodecane
N-Eicosane
N-Octadecane
N-Tetracosane
N-Tetradecane
P-Cymene
Pyrene
BPT
Daily
18,300.00
9,540.00
658.00
74,300.00
0.19
1.82
2.17
1.93
15.30
153.00
2.04
1.88
2.68
5.96
3.02
16.70
6.67
7.45
2.19
7.30
0.29
1.20
Monthly
8,600.00
6,090.00
294.00
74,300.00
0.19
1.82
2.17
1.93
15.30
153.00
2.04
1.88
2.68
5.96
3.02
16.70
6.67
7.45
2.19
7.30
0.29
1.20
BCT
Daily
18,300.00
9,540.00
658.00
N/A
N/A •
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A .
N/A
N/A
N/A
N/A
N/A
Monthly
8,600.00
6,090.00
294.00
N/A
N/A '
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
BAT
-JDaily
N/A
N/A
N/A
74,300.00
0.19
1.82
2.17
1.93
15.30
153.00
2.04
1.88
2.68
5.96
3.02
16.70
6.67
7.45
2.19
7.30
0.29
1 20
' , NSPS ' ' |
Daily
18,300.00
9,540.00
658.00
74,300.00
0.19
1.82
2.17
1.93
15.30
153.00
2.04
1.88
2.68
5.96
3.02
16.70
6.67
7.45
2.19
,7.30
0.29
1 20
-Monthly ||
8,600.00
6,090.00
	 	
294.00
	
74,300.00
0.19
1.82
2.17
1.93
15.30 1
	
153.00
2.04
1.88
2.68
5.96
3.02
16.70
6.67
7.45
2.19
7.30
0.29
1 20
                               6-10

-------
                                   Table6-7.
        Barge/Chemical & Petrokum Indirect Subcategory:  PSES and PSNS
                        Proposed Mass Based Limitations
                               Wf&myfM*
                               ^jafjtej'jgy^ ;U,' E"v"_ vZjt#
SGT-HEM
N/A
N/A
   347.00
   347.00
COD
N/A
N/A
.74,300.00
74,300.00
Cadmium
N/A
N/A
     0.51
     0.51
Chromium
N/A
N/A
     0.61
     0.61
Copper
N/A
N/A
    79.90
    79.90
Lead
N/A
N/A
     5.04
     5.04
Nickel
N/A
N/A
    39.10
    39.10
Zinc
,N/A
N/A
   241.00
   241.00
1-Methylphenanthrene
N/A
N/A
     9.70
     9.70
Bis (2-ethylhexyl) Phthalate
N/A
N/A
     2.05
     2.05
Di-N-Octyl Phthalate
N/A
N/A
     7.69
     7.69
N-Decane
N/A
N/A
     7.26
     7.26
N-Docesarie
N/A
N/A
     3.67
     3.67
N-Dodecane
N/A
N/A
    20.30
    20.30
-N-Eicosane
N/A
N/A
     8.13
     8.13
N-Octadecane
N/A
N/A
     9.07
     9.07
N-Tetracosane
N/A
N/A
     5.51
     5.51
N-Tetradecane
P-Cymene
Pyrene	
N/A
N/A
N/A
N/A
N/A
N/A
     8.90
     2.21
     2.94
     8.90
     2.21
     2.94
                                     6-11

-------
                 Table 6-8.
Truck/Food Subcategory: BPT, BCT, and NSPS
       froposed Mass Based Limitations
- -.;'.' • -. s" • Grams/Tank ^ , ~ "l f ^ --' "~'~ .'"/*' ~™ '>-'
Pollutant
BOD5
TSS
HEM
BPT
Dafly
Maximum
166.00
673.00
60.40
Monthly
Average
72.40
256.00
26.30
BCT
Dafly
Maximum
166.00
673.00
60.40
Monthly
Average
72.40
256.00
26.30
BAT
Daily
Maximum
N/A
N/A
N/A
" ' NSPS
Daily
Maximum
166.00
673.00
60.40
Monthly
Average
72.40
256.00
26.30
                 Table 6-9.
 Rail/Food Subcategory: BPT, BCT, and NSPS
       Proposed Mass Based Limitations
,.;. ; ';.-. -, Grams'/Tank „. '•,„, , " '* ' ^ '„ *
Pollutant
BOD5
TSS
HEM
BPT
Daily
Maximum
945.00
3,830.00
344.00
Monthly
Average
412.00
1,460.00
150.00
BCT
Daily
Maximum
945.00
3,830.00
344.00
Monthly
Average
412.00
1,460.00
150.00
, BAT .
- Daily
Maximum
N/A
N/A
N/A
" ' 'NSPS - "
Daily
Maximum
945.00
3,830.00
344.00
' Monthly
'Average
412.00
1,460.00
150.00
                Table 6-10.
Barge/Food Subcategory:  BPT, BCT, and NSPS
      Proposed Mass Based Limitations
V_;/" . Grams/tank ^ ^ "" " "" , J ' ^ *>», TI
Pollutant
BOD5
TSS
HEM
BPT
Daily
Maximum
945.00
3,830.00
344.00
Monthly
Average
412.00
1,460.00
150.00
BCT
Daily
Maximum
945.00
3,830.00
344.00
Monthly "
Average
412.00
1,460.00
150.00
BAT
%Dafly
Maximum
N/A
N/A
N/A
:* \ NSPS. ; ^
Dafly
Maximum
945.00
3,830.00
344.00
" Monthly
Average -
412.00
1,460.00
150.00
                   6-12
                                                                   _

-------
Appendices A.1 - A.7

-------

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Appendix D
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                 Appendix D
Assignment of Pollutants to Groups and Fractions
Fraction _
N/A
N/A
N/A .
Ni/A
N/A
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Base-Neutrals
Metal
Metal
Metal
Metal
Metal
Metal
Metal
Metal
Metal
Group ", ''
N/A
N/A .
N/A
N/A
N/A ' , ' '
Aromatics
Aromatics
Chlorobenzenes II
N-Paraffins
N-Paraffins
N-Paraffins
N-Paraffins
N-Paraffins
N-Paraffins
N-Paraffins '
N-Paraffins
PAHS
PAHS-
PAHS
PAHS
PAHS
Phthalates
Phthalates
Metals
Metals
Metals
Metals
Metals
Metals
Metals - ' .
Metals .
Metals
Analytic ;
BOD 5-Day
Chemical Oxygen Demand
(COD)
Total Suspended Solids
(TSS) ' ' -, .
Hexane Extractable
Material (HEM)
SGT-HEM
P-Cymene
Styrene
1 ,2-Dichlorobenzene
N-Hexacosane
N-Decane
N-Decosane
N-Dodecane
N-Eicosane
N-Hexadecane
N-Octadecane
N-Tetradecane
Anthracene
Fluoranthene
Phenanthrene
1 -Methylphenanthrene
Pyrene
Bis(2-Ethylhexyl)
Phthalate ,
Di-N-Octyl Phthalate
Titanium
Cadmium
Chromium
Copper
Lead
Nickel
Barium ,
Aluminum
Zinc '
CAS Numbec
C-002
C-004
C-009
C-036
C-037
99876
100425
95501
630013
124185
629970
112403
112958 .
544763
593453
629594
120127
206440
85018
832699
129000
117817
117840
7440326
7440439
7440473
7440508
7439921
7440020
7440393
7429905
7440666
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-------
-------
Appendix F
-------
-------
                  Appendix F
Percentile Estimates for Row per Tank Type Cleaned
Estimated Mi-dian Flow per Tank Type Cleaned (gals/tank) .
by Snbcategory and Type of Tanks [Cleaned
Subcategory
Barge Hopper
Rail Tank/Chemical
Tank Barge/Chemical
Truck Hopper
Tank Truck/Chemical
Tank Truck/Food
Tank Truck/Petroleum
Type of
Tanks Cleaned
Hopper
Hopper
Rail
Barge
Hopper
IBC
Hopper
Truck
Truck
Truck
Estimated
Percentile Value
712
1,830
2,091
4,857
144
123
842
605
790
193
95% Coverage Interval
Lower Upper Bound
Bound
70
846
1,032
1,742
6
73
206
494
597
193
10,451
3,456
2,471
9,106
748
261
1,011
972
1,097
193
                       F-1
-------
                                        Appendix F (Continued)
 Subcategory
 Type of
 Tanks Cleaned
Estimated
Percentile Value
                                                                      Lower
                                                                      Bound
95% Coverage Interval


             Upper Bound
 Barge Hopper
Rail Tank/Chemical
Hopper
                                                   7,970
                                  Hopper
                 4,360
                                  Rail
                                                   3,262
                                                                                  70
                                                                               1,363
                                                                               1,398
                                                     12,932
                                                    6,437
                                                    5,663
Tank Barge/Chemical
Truck Hopper
Barge
                                                   23,798
Hopper
                                                   595
                                                                               9,239
                                                    30,081
                                                                                 •57
                                                    938
Tank Truck/Chemical
                                                   260
                                  Hopper
                977
                                  Truck
                                                   1,211
                                                                                 147
                                                    457
                                                                                 785
                                                    1,260
                                                                                 661
                                                    1,910
Tank Truck/Food
                                  Truck
                                                                               1,343
                                                    1,343
                                               F-2
-------
Appendix F (Continued)
Estimated 90th PercentOe Flow per Tank Type Cleaned (gals/tank)
bjr Sufecategory and Type of Tanks .Cleaned - %-
Subcategory
Barge Hopper
Rail Tank/Chemical
Tank Barge/Chemical
Truck Hopper
Tank Truck/Chemical
Tank Truck/Food
Tank Truck/Petroleum
Type of
Tanks Cleaned
Hopper
Hopper
Rail
Barge
Hopper
me
Hopper
Truck
Truck
Truck
Estimated
Percentile Value
12,436
6,375
5,571
69,628
906
307
1,190
1,916
10,561
418
95% Coverage
Interval
Lower Bound Upper
Bound
3,523
3,345
2,482
52,996
573
203
888
948
10,561
296
14,421
7,192
6,148
114,730
1,032
728
1,362
3,641
10,561
439
          F-3
-------
Appendix F (Continued)

Subcategory
Barge Hopper
Rail Tank/Chemical
Tank Barge/Chemical
Truck Hopper
Tank Truck/Chemical
Tank Truck/Food
Tank Truck/Petroleum
iV^^^J^sw^^^^ty^C^^^ (s^tat^ ' •*_-.*„ ^~ "
Type of
Tanks Cleaned
Hopper
Hopper
Rail
Barge
Hopper •
me
Hopper
Truck
Truck
Truck
=s===^i
Estimated
Percentile Value
13,924
7U54
5,824
114,730
1,001
577
1,320
3,171
13,981
434 	
95% Coverage
Interval
Lower Bound Upper
Bound
5,361
4,061
2,581
62,188
684
257
902
1,203
13,981
298
14,917
7,239
6,309
138,688
1,064
818
1,396
3,797
13,981
445
       F-4
-------
r

--, -• ----- "•-• Estimated yytti
• 	 ', r - "• - by
Subcategory
Barge Hopper
RaU Tank/Chemical
Tank Barge/Chemical
Truck Hopper
Tank Truck/Chemical
Tank Truck/Food
Tank Truck/Petroleum
L Percentfle Flow per Tank Type Cleaned (gals/tank)
Subcategory and Type of Tanks Cleaned / >
Type of
Tanks Cleaned
Hopper
Hopper
RaU
Barge
Hopper
D3C
Hopper
Truck
Truck
Truck
Estimated
Percentile Value
15,115
7,260
6,342
153,062
1,076
842
1,408
3,797
16,716
447
95% Coverage
Interval
Lower Upper Bound
Bound
6,832
4,634
2,836
69,541
772
302
1,176
1,280
16,716
300
15,314
7,277
6,439
157,854
1,089
890
1,423
3,922
16,716
449
                                                                         F-5
-------
-------
Appendices G.l - G.9
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