### Via Electronic and Certified Mail

August 3, 2012

Mr. Ward Penberthy USEPA Headquarters Ariel Rios Building 1200 Pennsylvania Avenue, N.W.
Mail Code: 7405M Washington, DC 20460

Re: Follow Up to July 24, 2012 Siloxane Technical Working Group Teleconference

Dear Ward:

On behalf of the member companies of the Silicones Environmental, Health and Safety Council of North America (SEHSC), this letter with supporting documents follows up on the discussions during the July 24, 2012 Siloxane Technical Working Group teleconference concerning the development of an environmental monitoring program for D4 and D5. During the call SEHSC agreed to provide further

information on the scientific basis for the number of publicly owned treatment works (POTW) monitoring sites included in its environmental monitoring propos al. You also asked us to provide feedback on the Major Factors Relevant to the Environmental Presence of Siloxanes document the U.S. Environmental Protection Agency (EPA or Agency) shared during the teleconference and the Results from EPA’s Proposed D-Optimal Design of Experiment Analysis document EPA provided after the call (together referred to as EPA’s “Proposal”). Those items as well as some comments on the enforceable consent agreement (ECA) development process are pr ovided below for your consideration.

Scientific Basis for Number of POTW Monitoring Sites The intent of SEHSC’s March 1, 2012 environmental monitoring proposal is to lay out a monitoring program that would generate sufficient data to vali date the conceptual site models that were developed for D4/D5 manufacturing, processing and formulating sites and publicly owned treatment works (POTWs) and provide for a better understanding of the presence and concentration of D4 and D5 in the environment and sources of and pathways to the envir onmental concentrations of D4 and D5. Such information could then be used by the Agency to assess the risk, if any, D4 and D5 may pose to the

environment.

With respect to the POTW monitoring component of our proposal, attached is a report entitled Recommendation of Number of Sites to Sample for Validation of the D4 and D5 Publicly-Owned Treatment Works Conceptual Site Model which was prepared for SEHSC by ERM. This report, together with information included in Attachments 2 to 6 to this letter, provide support for the number of

### August 3, 2012 Mr. Ward Penberthy Page 2 of 3

POTW monitoring sites included in the March 1, 2012 environmental monitoring proposal submitted by
SEHSC.

Importantly, this report should also be considered in conjunction with SEHSC’s January 31, 2012

submission of the D4 and D5 Conceptual Site Models and Mass Balance Report which provided detailed information on the development of conceptual site models for D4 and D5 manufacturing, processing and formulating (MPF) facilities and for publicly owned treatment works (POTWs) that receive wastewater containing D4 and/or D5. The referenced report included detailed emission estimates and mass balances for MPF facilities based on an aggregation of facility-specific data supplied by SEHSC member companies. In the January 31 submission, SEHSC also provided information regarding existing D4/D5 monitoring pr ograms and available data, including industry’s Voluntary Product Stewardship Initiative (VPSI) and significant monitoring work conducted by government authorities in Canada and Oregon. All of th is information contributed to the development of the March 1, 2012 environmental monitoring proposal.

Feedback on EPA “Proposal” During the July 24, 2012 Siloxane Technical Working Group teleconference, SEHSC agreed to provide feedback on EPA’s “Proposal” regarding site select ion for the POTW monitoring component of an environmental monitoring program. Our comments on the Agency’s proposed approach are provided in Attachment 2, which incorporates Attachments 3 to 6 by reference.

Comments on ECA Development Process Based on the most recent discussions with EPA, SEHSC has some concerns about the ECA development process. As you know, SEHSC subm itted an initial D4/D5 environmental monitoring proposal to EPA on April 29, 2011. That proposal wa s crafted based on industry’s discussions with EPA regarding the Agency’s interest in, among other things, “a better understanding of the sources of and pathways to the environmental concentrations of D4/D5 in environmental media and biota.” EPA provided its feedback on the proposal via September 19, 2011 letter with attachment from Maria Doa to Karluss Thomas (Doa Letter). SEHSC used this feedback and subsequent discussions with the Agency to help guide its preparation of the technical documentation submitted to EPA on January 31, 2012 and the revised environmental monitoring proposal submitted on March 1, 2012.

On March 7, 2012, SEHSC met with representatives of the Agency to provide a presentation on the revised environmental monitoring proposal and to address any questions regarding the scope/contents of the proposal. Following this meeting, based on feedback from the Agency, it was our understanding that EPA believed that the revised proposal represented a reasonable basis to proceed with the development of an ECA. Indeed, the May 24, 2012 Federal Register notice (FR Notice) announcing a public meeting to kick off the ECA negotiation proc ess states that EPA has evaluated the environmental monitoring proposal submitted by SEHSC on behalf of its members and the Agency believes that “proceeding with the negotiation of a consent agreement is an efficient means of developing the data.”

In light of the above, SEHSC’s members were extremely surprised and concerned to learn during the July 24, 2012 Siloxane Technical Working Group tele conference that EPA’s “Proposal” is that 42 sites be included in POTW portion of the environmental monitoring program rather than the 5 sites described in our proposal. There are, of course, significant technical, practical, and cost ramifications to such an extensive expansion of the scope of an environmental monitoring program. Given that on just this one aspect of the environmental program EPA is expre ssing a desire for a program that is over 8 times more expansive than what was proposed, we are confused as to the basis for the prior determination that SEHSC’s proposal constituted a reasonable bas is for moving forward with the negotiation of an ECA. Equally importantly, such a drastic expansion of the monitoring program is not necessary to

### August 3, 2012 Mr. Ward Penberthy Page 3 of 3

achieve the goal of assessing potential for risk to the environment from D4 and D5 discharges from manufacturing, processing, formulation sites and from high volume industrial and commercial end uses.

When considered in the context of the tremendous am ount of information that already exists on the environmental discharges, fate and effects of D4 / D5, SEHSC’s proposal would clearly provide sufficient data to assess whether there is such a potential risk.

SEHSC has already expended significant time and resources in compiling technical documentation and preparing environmental monitoring proposals to address the Agency’s “concerns regarding the environmental effects of D4 and D5” and determination that “[e]nvironmental monitoring could help develop a better understanding of the potential effe cts of these chemicals in the environment.” 1 

### Throughout the proposal development process, SEHSC has been very transparent with EPA. We have provided extensive data and information regarding the behavior of D4/D5 in the environment and we have consistently sought out opportunities to meet with the Agency to explain the state of the science regarding D4/D5, as well as the scope, purpose, and basis for our proposed monitoring program.

SEHSC prepared the March 1, 2012 proposal in good faith based on the feedback received to date from the Agency taking into account best available science regarding the substances. We believe that our proposal represents an unprecedented commitment by industry to conduct extensive environmental monitoring under an ECA on substances which we believe, and other regulatory authorities have concluded, do not pose any appreciable risk to the environment. We believe that our proposal would provide the Agency with sufficient information for it to better understand the potential environmental effects of the substances and assess the risk, if any, the substances may pose to the environment.

Accordingly, we are very concerned about the Agency responding at this point in the ECA negotiation process with a proposal that differs so substantially on such a fundamental point as number of monitoring sites from the March 1, 2012 proposal by SEHSC which was to serve as the basis for the negotiation. The intent of our monitoring proposal was never to conduct an extensive research program at POTW sites which we believe would be more appropriately addressed in a different venue under other regulatory authority. Rather, it was to develop data to enable the Agency to assess the risk, if any, D4/D5 pose to the environment. In that regard, we stand ready to continue to work with EPA and other interested parties in developing a realis tic environmental monitoring program that takes

into account all of the available scientific information on the behavior of these chemicals in the environment. We trust that the upcoming teleconference on August 7 th 

### will be productive to that end. In the meantime, if you have any questions regarding this submission, please do not hesitate to contact me at (703) 788-6570.

Sincerely,

Karluss Thomas Executive Director, SEHSC

Enclosures

1 77 Fed. Reg. 31009 (May 24, 2012).

### Delivering sustainable solutions in a more competitive world 

# Recommendation of Number of Sites to Sample for Validation of the D 4 

# and D 5 

# Publicly-Owned Treatment Works Conceptual Site Model

Silicones Environmental, Health and Safety Council Herndon, VA 20171

July 2012

www.erm.com 

## Final ReportSilicones Environmental, Health and Safety Council



# Recommendation of Number of Sites to Sample for

Validation of the D 4 

# and D 5 

# Publicly-Owned Treatment Works Conceptual Site Model

July 2012

ERM Project No. 0151161



### Salvatore T. Giolando, Ph.D. Partner-in-Charge

Donna D. Morrall, Ph.D., Project Manager

Elsie F. Millano, Ph.D., P.E., Project Manager

Environmental Resources Management
1701 Golf Road, Suite 1-1000 Rolling Meadows, IL 60008

www.erm.comDRAFT



### TABLE OF CONTENTS

EXECUTIVE SUMMARY ................................................................................................V

1.0 .................................................................................................1 INTRODUCTION

1.1 ........................................................................................1 BACKGROUND

1.2 ..................................................1 POTW CONCEPTUAL SITE MODEL

1.3 ..........................................................2 PRIMARY EXPOSURE ROUTES

2.0 .................................................4 CONCEPTUAL SITE MODEL VALIDATION

2.1 .............................................................................................4 OBJECTIVES

2.2 .4 DETERMINATION OF NUMBER OF POTW SITES TO SAMPLE

3.0 ...................................................................................................8 CONCLUSIONS:

LIST OF FIGURES

Figure 1 Generic Block Flow Diagram for POTWs Receiving Wastewater Containing D 4 

### or D 5



### Figure 2 Generic Conceptual Site Model for Human and Ecological Receptors for POTWs Receiving Wastewater Containing D 4 

### or D 5

### LIST OF ACRONYMS

CSM Conceptual Site Model

cVMS Cyclic volatile methyl siloxanes

D 4 

### Octamethylcyclotetrasiloxane

D 5 

### Decamethylcyclopentasiloxane

DERR Department of Environmental Response and

Revitalization

EPA Environmental Protection Agency

ERM Environmental Resources Management, Inc.

MPF Manufacturer, Processor, and Formulator

NIST National Institute of Standards and Technology

POTW Publicly Owned Treatment Works

WWTP Waste Water Treatment Plant

SEHSC Silicones Environmental, Health and Safety Council

### EXECUTIVE SUMMARY

Environmental Resources Management (ERM) was retained by the Silicones Environmental Health and Safety Council (SEHSC) to recommend the number of Publicly-Owned Treatment Works (POTWs) to sample for verification of the D 4 

### and D 5 

### generic Conceptual Site Model (CSM) for POTWs (the “POTW CSM”). The POTW CSM was developed by ERM and included in ERM’s D 4 

### and D 5 

### Conceptual Site Models and Mass Balance Report , dated January 2012. The January 2012 report included a generic block diagram for a typical POTW, with the assumption that it had only (1) primary and secondary treatment (activated sludge) and effluent chlorination for the liquid waste before discharge to a surface water body; and (2) blending and st orage, anaerobic digestion, and storage for the sludge produced during primary and secondary treatment (“biosolids”), followed by beneficial use of the biosolids as a soil

amendment. Advanced treatment POTWs were not modeled because advanced technologies are more likely to remove D 4 

### and D 5 

### more completely than conventional technolo gies. This selection is reasonable because (1) according to USEPA, approximately 50% of the POTWs utilized only secondary treatment technologies and served 41% of the population in the U.S. (USEPA (2008). Clean Watershed Needs Survey 2008, Report to Congress , EPA 832-R-10-002.); and (2) the absence of advanced treatment increases the likelihood that there could be some remaining D 4 

### and D 5 

### in the effluent and/or the sludge.

To validate the exposure pathways depicted in the POTW CSM and provide additional information on th e presence and concentration of D 4 

### and D 5 

### in the environment, environmenta l monitoring is being proposed by SEHSC member companies at select locations. In order to support the development of a detailed samplin g plan, ERM has prepared this Recommendation of Number of Sites to Sample for Verification of the D 4 

### and D 5 

### Publicly-Owned Treatment Works Conceptual Site Model (the “Selection of Number of POTWs to Sample report”), which documents ERM’s procedures and recommendations.

Conceptual site models are commonly developed to describe a specific site, but the POTW CSM represents a generic POTW. Therefore, ERM recommends that several types of POTWs be selected for sampling (including activated sludge [AS], ox idation ditch [OD], lagoon [LA], and trickling filter [TF] sites). As there is no standard statistical guidance on

the number of sites that should be used to determine whether the CSM adequately represents POTWs in general, ERM used standard statistical analysis procedures developed for de termination of a mean and standard

### deviation

. 

### Based on a standard statistical approach, we recommend sampling a total of five sites (inclu ding two AS, one OD, one LA, and one TF sites) to verify the key sources, receptors, and exposure/distribution pathways for D 4 

### and D 5 

### at POTWs and to augment the existing information on the presence of D 4 

### and D 5 

### in the environment.

Data from these five sites will augment the large volume of data already available on the physical/chemical properties, fate and distribution of D 4 

### and D 5 

### in several countries, as well as the data to be obtained for the four industry sites which include manufa cturer, processer, and formulator (MPF) plants with on-site wastewate r treatment. The POTWs chosen for this study will serve a variety of popu lation areas and have low effluent

dilution factors. Other factors such as minimization of volatilization during treatment plant operation, w ill be considered during development of specific sampling plans. The inte nt is to select sites and develop sampling plans for these sites th at maximize the occurrence and concentration of D4 and D5 in wastewater effluents and receiving waters and will therefore, represent worst- case scenarios.ERM 

### 1 July 2012 

### that 1.0 INTRODUCTION

1.1 BACKGROUND

Octamethylcyclotetrasiloxane (D 4 

### ) and decamethylcyclopentasiloxane (D 5 

### ) are chemical products from the broa der family of cyclic siloxanes have a wide range of industrial and consumer applications in the United States and internationally. Both of these substances are manufactured, formulated and/or processed at a numb er of sites in the United States. The Silicones Environmental, Health and Safety Council (SEHSC) retained Environmental Resources Management, Inc. (ERM) to prepare the following work products:

 

### A block flow diagram and conceptual site model (CSM) for publicly owned treatment works (POTWs) for use in the evaluation of emissions from POTWs receiving D 4 

### and/or D 5



### .

 

### A strategy for validation of the exposure pathways described

for D 4 

### and D 5 

### by the POTW CSM.

A detailed description of the development of the POTW CSM is provided

in the D 4 

### and D 5 

### Conceptual Site Models and Mass Balance Report (the “ERM CSM report”). As part of the strategy for validation of the POTW CSM, ERM prepared this Selection of Number of POTWs to Sample Report to describe the procedure used to determine the recommended number of POTWs that should be sampled in order to validate the exposure/distribution pathways described for D 4 

### and D 5 

### in the POTW CSM and provide additional info rmation on the presence and

concentration of D 4 

### and D 5 

### in the environment

.

1.2 POTW CONCEPTUAL SITE MODEL

The POTW CSM, illustrated in Figure 1, provides a working hypo thesis of the sources, receptors, and pathways of relevance for D 4 

### and D 5 

### . This model represents processes typical of secondary treatment plants in the

United States. As shown in the table below, activated sludge (AS) plants serve 79% of the US population an d treat approximately 81% of the discharges to POTWs. Oxidation ditc h (OD), lagoon (LA), and trickling filter (TF) plants combined serve approximately 19% of the US population and treat 17% of the wastewater discharges to POTWs.ERM 

### 2 July 2012 Treatment Type* AS OD TF RBC LA P

T otal flow in the US (%) 81 5 6 2 6 0 Total facilities in the US (%) 47 10 8 3 32 0 Population Served in the US ( % ) 79 6 7 2 6 0

*2004 US EPA Needs Data from ISTREEM AS = activated sludge, OD = oxidation ditch, TF = trickling filter, RBC = rotating biological contactor, LA = lagoon, P = primary



### In addition to AS plants, which wer e used to develop the POTW CSM, OD, TF, and LA plants will also be sampled to verify that the same fundamental concepts and pathw ays are relevant for these treatment types. Rotating Biological Contactors (RBC) and Primary (P) treatment plants are not recommended for sa mpling to verify the POTW CSM because they serve only approximately 2% of the population.

1.3 PRIMARY EXPOSURE ROUTES

The generic POTW CSM diagram for human and ecological receptors is shown in Figure 2. Exposure pathways for POTW workers were not included because in the U.S., these exposures are subject to regulation by the Occupational Safety and Health Administration. The processes and resulting p athways identified in the diagram may not occur at each of the POTWs that receive wastewater containing D 4 

### and/or D 5 

### ; the application of this diagram to any specific POTW is subject to verification as to the processes used.

Based on the fate data and emissions mass balance of the CSM for D 4 

### and

D 5 

### industrial uses, the pathways expe cted to be complete at POTWs are:

 

### Inhalation exposure resulting from dispersion in air (this pathway applies only to human receptors, because ecological receptors are not typically evaluated for inhalation exposure risk); and

 

### Exposures via sediment, includin g direct exposure for bottomdwelling biota and indirect exposure via the food chain to ecological receptors.

However, in order to fully validate the model and provide additional information on the presence and concentration of D 4 

### and D 5 

### in the
environment, we recommend to incl ude air sampling at one site andERM 

### 3 July 2012 

### influent/effluent, biosolids, surface water, sediment, benthic organism, and marker fish at each site/associated receiving water. Since the composition of benthic communities and fish species in any receiving water is somewhat unique to each lo cation, the specifics of their sampling would be developed during the preparation of sampling plans.ERM 

### 4 July 2012 

### 2.0 CONCEPTUAL SITE MODEL VALIDATION

2.1 OBJECTIVES

The CSM provides a hypothesis and description of applicable POTW exposure pathways and the trans port and disposition of D 4 

### and D 5 

### along those pathways. Validation involves evaluation of data from a POTW which can be used to verify that the model compartments and pathways are appropriate representations of the system (in this case, sampling results from environmental media). The CSM can then be adjusted and refined accordingly.

To verify that the POTW CSM is generally representative, ERM recommends that several additional POTWs be selected for sampling. There is no standard statistical guid ance on the number of sites that should be used to determine whether the CSM is of general applicability. Therefore, ERM relied on a statistical procedure used to determine the appropriate number of samples to collect for calculation of mean values and standard deviations of the mean for pathways in the CMS.

2.2 DETERMINATION OF NUMBER OF POTW SITES TO SAMPLE

According to Rykiel (Rykiel, Jr., Edward J. Testing ecological models: the meaning of validation. Ecologic al Modelling. 1996. 90: 229-244) “a valid model is one whose scientific or concep tual content is acceptable for its purpose.” He notes that validation requires that the modeler specify the purpose of the model, the criteria th e model must meet to be considered acceptable, and the context in which the model is intended to operate. As discussed earlier, the purpose and cont ext of the model are to represent the flows and pathways of D 4 

### and D 5 

### in POTWs with secondary treatment. The model will be considered acceptable if monitoring data indicate that the primary pathways are represented in the model and that the model correctly indentifies the relative importance of the exposure pathways for D 4 

### and D 5 

### . The sampling program developed to qualitatively validate the conceptual site model will concurrently provide quantitative values for the removal pathways and resultant environmental

concentrations.ERM 

### 5 July 2012 

### Validation of the CSM requires that me an concentrations be calculated for individual sources and pathways includ ed in the CSM. The sample size required to estimate the mean concen trations will be influenced by the

value selected for α (the risk of rejecting a true hypothesis, or Type I error) and the population standard deviation ( σ ). The following explanation of the required sample size for calculat ion of the mean was obtained from

the National Institute of Standards and Technology (NIST)/SEMATECH eHandbook of Statistical Methods

, http://www.itl.nist.gov/div898/handbook/index.htm, updated in April

2012.

The basic equation to calculate the error associated with the mean is:

 

###  = z α /2 

### * ( σ * √ n )

Where:

 

###  = difference between observed and true mean

 

### z α /2 

### = critical value of the standard distribution curve

 

### σ = standard deviation of the population

 

### n = sample size

The equation above can be rearranged as follows to obtain n:

 

### n = [(z

α /2 

### * σ )/  ] 2



### A restriction is that the standard de viation must be known. Lacking an exact value for the standard deviation requires some adjustment, such as using the best estimate from a previous experiment.

It is usual to state the shift,  , in units of the stand ard deviation, thereby simplifying the calculation. For a shift or error equal to one standard deviation the equation reduces to

 

### n = [z

α /2 

### ] 2 

### ≈ 4

for a 95% confidence interval ( α = 0.05) and z = 1.96, which is the critical value from the normal distribution (z) for 1- α /2 = 0.975.

Designation of the alpha levels can vary. Ecologists, by convention, use a probability level of 0.05 to test for statistical significance. However, there is no objective basis for this stan dard (Rykiel, 1996). According to the Ohio Environmental Protection Agency (EPA) Division of EnvironmentalERM 

### 6 July 2012 

### Response and Revitalization (DERR) alpha levels 0.05-0.2 are recommended for ecological risk assessment.

Following standard statistical and EPA convention, we used an alpha of

0.05 for determination of the mean within one standard deviation. This results in a recommended sample size of 4. ERM recommends sampling
two AS, one OD, one TF, and one LA. Th is would result in a total of five POTWs with at least one representati ve from each treatment type that

serves ≥ 6% of the US population. The additional AS plant would be sampled because they occur most freque ntly and to provide a better set of

data. The POTWs will be selected based on factors that maximize the

occurrence and concentration of D4 and D5 in wastewater effluents and receiving

waters

. The data from the five POTWs sampled as part of this study will add to the already available global database of D 4 

### and D 5 

### environmental concentrations. As a result of previous efforts, there is a large amount of information available on the physical/chemical properties, fate, and

removal of D 4 

### and D 5 

### including studies in the US, Canada, and Norway. In Oregon, 52 effluent sites were samp led and all sites were non-detect for

D 4 

### and D 5 

### (Oregon DEQ SB737 Fact Sheet, 2011). Eleven waste water treatment plants (WWTP)were sample d in Canada in 2010. The WWTPs included different treatment types in cluding lagoon, secondary activated sludge, and chemically assisted primary treatment. The detailed results of this study are reported in Wang et al . Concentrations of cyclic volatile methyl siloxanes (cVMS) in various environmental media from Southern Ontario and Southern Quebec were measured. Approximate removal efficiency of cVMS for all WWTPs was greater than 92% from wastewater,

and mean removal efficiencies of D 4 

### and D 5 

### were 98%. Resultant D 4 

### and

D 5 

### concentration ranges in efflue nt were <0.006–0.045 μg/L and <0.004– 1.56 μg/L, respectively. The different treatment types of WWTPs had little

influence on the removal efficien cy of cVMS between 10 and 25 o 

### C.

Removal rates from lagoons were highly variable at temperatures ≤ 5°C,

ranging from 25% to 99% at the 11 sites tested. 

### [Wang, D., H. Steer, T. Young, T. Tait, Z. Williams, G. Pacepavicius, T. Ng, S. Smyth, L. Kinsman,
E. Sverko, L. Svobda, S. Fazal and M. Alaee, 2010. Concentrations of cyclic volatile methyl siloxanes (cVMS) in va rious environmental media from Southern Ontario and Southern Quebec . Burlington, ON, Canada: Environment Canada, Water Science and Technology Directorate. December 16, 2010].

POTWs chosen for the monitoring program will represent those treatment types that are most prevalent in the US and serve the greatest percentage of the US population. However, base d on these findings, little differenceERM 

### 7 July 2012 

### is expected in removal efficiencies for the POTWs that will be sampled as part of this proposed study.ERM 

### 8 July 2012 

### 3.0 CONCLUSIONS:

The goal of the POTW monitoring study is to validate the exposure pathways
depicted in the POTW CSM and provid e additional information on the presence

and concentration of D4 and D5 in th e environment. To accomplish this, ERM proposes collecting samples from a total of five POTWs. The five
POTWs would include two AS, one OD , one TF, and one LA. The sites

will be selected based on factors that maximize the occurrence and concentration of D4 and D5 in wastewater effluents and receiving waters and will therefore represent worst case scenarios.

It is anticipated that a rigorous sampling protocol including sampling locations, parameters to analyze, analytical methods, sample type (composite or grab), etc., will be designed for each POTW before samples

are collected. The intent is to sele ct sites and develop sampling plans for these sites that allow for the validation of the CSM through assessment of

the occurrence and concentrations of D4 and D5 in the proposed

environmental media.

The results from the POTW validatio n study will complement the data gathered for the four industry site s (including manufacturer, processer, and formulator plants) with on-site wastewater treatment in the US and will complement the data available from existing and on-going monitoring programs in the US and elsewhere.

# Figure 1

# Figure 2

# ATTACHMENT 2Page 1 of 4 

### Attachment 2

Comments on EPA’s Proposed Approach for POTW Site Selection

EPA Comment: “ Charge: Build on the SEHSC proposal to develop a science-based, rational proposal for monitoring of Publicly Owned Treatment Works (POTW) sites which provides sufficient information and data on the presence and concentrations of D4 and D5 in the environment near POTWs, to support

a national risk assessment .”

Industry Comment: Extensive data already exists that characterizes the presence and concentrations of D4 and D5 in the environment near POTWs. Real world global monitoring data (Lake Pepin, MN, USA, Norway, Sweden, Canada and Japan) of D4 and D5 in sediment is available and most (exception is Japan) has been provided to EPA. In addition, recent monitoring results for D4 and D5 in surface sediments of Tokyo Bay, Japan are now becoming av ailable (summary provided in Attachment 4). These data have been used to calibrate and validate environmental models that allow for prediction of

environmental concentrations as well as conduct of a probabilistic risk assessment (Attachment 3; Woodburn, 2012).

Industry Comment: This extensive data set already collected has also been used to conduct a probabilistic risk assessment to compare field exposure levels of D4 and D5 in sediment to toxicity trigger levels for these compounds as determined in laboratory chronic toxicity tests with benthic organisms (Woodburn 2012). This risk analysis indicate s that existing D4 and D5 sediment residues pose virtually no quantifiable risk to benthic invertebrate species, as concentrations do not exceed interpolated, 5 th 

### centile chronic NOEC values.

Industry Comment: Monitoring results from Tokyo Bay, Japan (summary provided in Attachment 4) demonstrate that concentrations of D4 and D5 in surface sediments of the receiving waters are greatest near the source of discharge and decrease to levels less than or equal to the detection limit with increasing distance from the source of discharge. Hence, probability based sampling at a single well selected worst-case study area would itself be sufficient to identify the range and distribution of concentrations that could potentially exist in surface sediments impacted by wastewater discharges. Probability based sampling provides precise estimates of mean concentration such that coefficients of variation (CV) are typically less than 10%. Consequently, probability based sampling at a few study areas may be used to derive precise estimates and confidence intervals for concentrations that could potentially exist in surface sediments impacted by wastewater discharges from different types of POTWs. Margin of Error (MoE) power calculat ions (Attachment 6) for n=5 locations having an

estimated CV=10% indicate that the 95% confidence interval for the mean concentration would be ±12% for the population of POTWs from which the 5 locations were selected.

EPA Comment: “ Monitoring Objective: A sampling strategy should collect monitoring data that scans a variety of POTWs that have attributes that could be used to characterize the “likely range” of siloxane environmental concentrations around POTWs. With these data, an understanding of how population size, receiving water body flow and other factors contribute to the environmental loading of siloxanes near POTWs would be developed.”

Industry Comment: Extensive data already exists that characterizes the “likely range” of siloxane concentrations that would be expected around POTWs. Real world monitoring data (Lake Pepin, MN, USA, Norway, Sweden, Canada, and Japan) of D4 and D5 in sediment is available and has been provided to EPA that demonstrates this. In addition, recent work conducted in Tokyo Bay (Attachment 4, Powell, 2012) supports that concentrations of D4 and D5 in surface sediments will be log-normallyPage 2 of 4 

### distributed over the study area, with concentration s decreasing with increasing distance from pointsource discharges at the head of the bay. Results from the systematic stratified design resulted in very precise estimates of mean concentrations of the cVMS materials in surface sediment of the study area in Tokyo Bay. Probability based sampling at a single well selected worst-case study area would itself be sufficient to identify the likely range and distribution of concentrations that could potentially exist in surface sediments impacted by wastewater dischar ges. Moreover, probability based sampling at a few study areas that includes a variety of POTWs may be used to derive precise estimates and confidence intervals for concentrations that could potentially ex ist for surface sediments impacted by wastewater discharges from different types of POTWs.

Industry Comment: The behavior of volatile siloxanes during wastewater treatment can be reasonably predicted due to their characteristic physicochemical properties. A robust model such as SimpleTreat can be used for further in-depth analysis such as uncertainty analysis. This currently is in progress by the Industry in support of the POTW monitoring to be conducted under the Voluntary Product Stewardship Initiative (see March 1, 2012 submission).

EPA Comment: “ Approach: The major factors in site selection likely to affect the measured levels of

siloxanes in the sediment at outfalls from POTWs outside of SEHSC control would be established. These factors would then be used as input to an optimization process to develop a monitoring program design. This optimization method, D-Optimal design, coupled with the relevant major factors established, would provide a scientific basis for a monitoring program design to understand how the various factors contribute to siloxane measured results without testing the entire set of possible permutations. Additional sampling considerations such as air monitoring and seasonal variability would be considered in the future, but do not impact site selection and are not

included here .”

Industry Comment: Industry agrees that additional sampling consideration such as air monitoring and seasonal variability do not impact site selection and need not be included for consideration at this time.

EPA Comment : Above and Background on Proposed D-Optimal Design of Experiment

“Background: The “Design of Experiment” (DOE) theory was developed during the early portion of the 20th century as a method of applying statistics before an experiment is conducted to maximize the benefit of the experiment while minimizing the number of samples. A particularly powerful approach applied the “D-Optimal” criterion and was coded into a FORTRAN computer algorithm by Mitchell (1974). This approach is available today through commercial software packages such as Design Expert (Stat-Ease, 2012). D-Optimal DOE has been widely used in agriculture, manufacturing, and the biomedical industry. D-Optimal DOE has been used in a few studies to design sampling experiments for environmental contaminants (e.g., Gribik et al., 1976). This is indeed an effective optimization technique with a demonstrated ability to minimize the number of required samples while generating a representative dataset.”

Industry Comment: Industry acknowledges that a design of experiment (DOE) approach is an effective optimization technique and utilized this approach in the first submission to EPA (April, 2011). Further refinement of this approach taking into consideration the robust data available on the physicochemical properties of D4 and D5 is attached (Attachment 5; Kim, 2012). In order to employ a D-Optimal Design approach, a set of input parameters (or factors) is required which influence the output (i.e., environmental concentration of D4 and D5). Major factors are selected by the following three criteria (CR).

 CR 1: Major factors should be selected based on proper understanding or evidence of the inputoutput relationship.Page 3 of 4 

###  CR 2: The selected factors should be independent of each other.

 CR 3: The quantity of the factors should be known prior to the monitoring program.

To apply the criteria, it is critical to assess the ph ysicochemical properties of D4 and D5 (i.e. highlyvolatile, practically insoluble in water, preferential partitioning to organic phases, and nonbiodegradable). If this understanding did not exist already then it may be appropriate to utilize a broader range of factors such as what was done in the EPA’s proposal that are “ likely to affect the

measured levels of siloxanes in the sediment at outfalls from POTWs ”. Given the knowledge publically available, and provided to EPA, a more sy stematic evaluation of factors is warranted.

EPA Comment: “ Major Factors Relevant to the Environmental Presence of Siloxanes

Below is a list of major factors related to site selection that EPA believes most strongly impact the

siloxane levels in the sediment and water at POTW outfalls.

Influent sources 1. Population size served by the POTW (High/Medium/Low) This factor could provide information on how consumer or household use of siloxanes contributes to POTW siloxane loadings.

POTW characteristics 2. Treatment types (activated sludge, other secondary treatment types). Other secondary treatment types could include trickling filter, oxidation ditch or lagoons. A focus would be placed on POTWs that use sec ondary treatment because this would represent the vast majority of POTWs across the nation Tertiary treatment is not included because it is assumed there would be very low siloxane

concentrations in effluent. 3. High Total Suspended Solids (TSS) in effluent. Indications of high TSS in effluent from the POTW could be important as we know that siloxanes partition to solids.

Receiving body characteristics 4. Geography (North, South) Geography could be important as colder temperatures likely decrease the amount of siloxanes that partitions to the air and will increase the amount that remains in the water and that reaches the

sediment. 5. Receiving body flow (lake/ low flow stream/high flow river) Receiving body flow directly impacts the degree of dilution and probably the turbulence in the water. Both could impact the partitioning behavior of siloxanes. 6. Organic content of receiving body (high/low) High organic content in the receiving body could increase the amount of siloxanes that partition

to the sediment.”Page 4 of 4 

### Industry Comment:

We have applied three criteria to evaluate six major factors proposed by the USEPA. All the factors of the USPEA are incomplete to be used as inputs for a D-Optimal procedure. Instead, we proposed a new set of input factors as listed below.

 Input variables 1) Treatment type (3 levels: low/medium/high for TSS concentration and solid phase D4/D5 concentration): for example, a. Low: activated sludge with bubble aeration (i.e., low TSS concentration and solid phase D4/D5 concentration) b. Medium: a trickling filter c. High: a lagoon without aeration (i.e., high TSS concentration and solid phase D4/D5 concentration) 2) Dilution factor (2 levels: low/high)

 Input constant 3) Predicted influent total concentration of D4/D5: only high concentration is needed. We can estimate effluent concentration from the same treatment type assuming the removal efficiency is same for the same type.

Thus, all six combinations (3×2, the full factorial) are suggested according to the new proposal. The total number could be reduced by a D-Optimal procedure (possible to three). The monitoring program will measure actual D4/D5 concentration in water and TSS of effluent and receiving water, TSS concentration in effluent, and organic content in sediment of receiving water body.

# ATTACHMENT 3Page 1 of 24 

### Attachment 3

Woodburn, 2012

Benthic Invertebrate Exposure and Chronic Toxicity Analysis for cVMS Materials – A Probabilistic Risk Assessment Approach

Kent B. Woodburn and David E. Powell Dow Corning Corporation Midland, MI 48686

Abstract

The objective of this study was to use probabilistic risk assessment techniques to compare field exposure levels of cyclic volatile methylsiloxanes materials octamethylcyclotetrasiloxane (D4) and decam ethylcyclopentasiloxane (D5) in sediment to toxicity trigger levels for these compounds determined in laboratory chronic toxicity tests with benthic organisms. The lipophilic nature of these cVMS materials (i.e., log Kow >6) suggests that sorption to sediment organic carbon is a likely removal mechanism of these compounds from water. The exposure data for D4 and D5 in

sediment were sorted and the 95 th 

### centile exposure levels in sediment, both on a dry weight (dw) and organic carbon (OC) basis, were calculated. These concentrations were then compared to interpolated, highly s ensitive 5th centile benthic sediment NOEC trigger concentrations, calculated from sorted distributions of chronic assays using a variety of benthic species. Neither material showed any overlap of sediment 95 th 

### centile

levels to 5 th 

### centile NOEC values and safety factors separated the unlikely (i.e., 0.25%)events. Body burden NOEC values were calculated for D4 and D5 using field worstcase BSAF values of 20 and 5, respectively, to calculate chronic NOEC 5 th 

### centile levels on the basis of mass of chemical per lipid mass of organism. Again, no overlap between measured exposure 95 th 

### centile concentrations and interpolated, highly sensitive chronic NOEC values were observed for either compound. This risk analysis indicates that existing D4 and D5 sediment residues pose virtually no quantifiable risk to benthic invertebrate species, as concentrations do not exceed interpolated, 5 th 

### centile chronic NOEC values. Further research could explore the applicability of this relationship to other benthic species and also exposure the absence of apparent toxicological impact of D4 and D5 on benthic invertebrate organisms even at concentration levels exceeding their respec tive OC solubility values in sediment.

Introduction Benchmarking concentrations versus toxicity threshold levels

Natural and anthropogenic materials enter the environment on a regular basis, and it is frequently of interest to estimate the probability or likelihood of adverse effects of a chemical to wildlife organisms. The simplest approach to assessing the potential for

environmental effects of a chemical is to compare a measure of exposure with some threshold for detrimental effects. On a screening basis, this can be done using the calculation of a simple hazard quotient (HQ), where a point estimate of the measured or predicted exposure concentration is divided by a toxicity reference value; generally the threshold or safe level is determined from a dose-response relationship in a chronic

exposure:Page 2 of 24 

### HQ = Exposure concentration/Effect concentration or chronic NOEC (1)

The HQ method represents the initial tier of an assessment, meant only to rebut the presumption of potential adverse effects. If the HQ>1, the potential of risk cannot be rebutted nor quantified in terms of probability or likelihood. Since conservative assumptions apply to both exposure and toxicity endpoints in the HQ assessment, the method is inherently protective in nature, but not predictive as to the level of potential risk. The term “risk” implies an element of likelihood or probability, which cannot be calculated from such point estimates. A tiered risk assessment process has been proposed and endorsed by the US EPA (ECOFRAM, 1999), in which increasingly complex and resource-intensive levels of assessment are conducted. The lower tier assessments utilize conservative assumptions and estimates of parameters, along with possible safety factors. By design, these estimates are highly conservative and produce very protective HQ values. Higher tier , probabilistic risk assessment (PRA) methods allow the risk assessor to include stochastic properties of both exposure and response. The use of PRA expresses a continuum of potential exposure and effect and a range of certainty can be calculated for overlap, or lack thereof. The probabilistic method has been applied in numerous environmental risk assessments for North American systems (Giesy et al., 1999; Klaine et al. 1996; Solomon et al., 2000), as well as Europe (Hart, 2001). This type of assessment has been used for several years to derive federal water quality criteria (Stephan et al., 1985), generally utilizing a 5 th 

### centile no-observed effect concentration (NOEC) from chronic toxicity studies as the target effect level.

Body burden concentrations of chemicals are not a commonly-used endpoint in risk assessments for aquatic contaminants, as nearly all toxicity data for fish are expressed in terms of aquatic concentrations. However, organism body burdens potentially provide an exposure metric that may be more strongly correlated with effects (McCarty and Mackay, 1993). The correlation is most prevalent for chemicals that demonstrate bioaccumulation in fish and other biota to levels in greater than water concentrations. Research indicates that at naturally-occurring food/water concentration ratios, uptake of highly lipophilic chemicals (i.e., log K OW 

### >6) from water into biota may be low compared to uptake via consumption of contaminated foodstuffs, with the importance of dietary uptake increasing with increasing K
ow 

### values (Thomann, 1989; Qiao et al., 2000). Modeling of field data from TMF studies in Lake Pepin, MN (Powell et al., 2008) on cyclic volatile methylsiloxanes (cVMS), such as decamethylcyclopentasiloxane (D5), revealed that food uptake accounted for greater than 90% of accumulated D5 residues in fish (HydroQual, 2009).

Objective

The objective of this study was to compare the field exposure levels of octamethylcyclotetrasiloxane (D4) and decam ethylcyclopentasiloxane (D5) in sediment to toxicity trigger levels for these compounds determined in laboratory chronic toxicity tests with benthic organisms. The lipophilic nature of these cVMS materials suggests that sediment adsorption is a likely remo val mechanism of these compounds from water and empirical evidence supports this concept (Norden, 2005; NILU 2007). Screening risk assessments on D4 (Environment C anada, 2008a) and D5 (Environment Canada, 2008b) have been completed by Environment Canada, but risk quotients for sediment were not explicitly examined. Therefore, probabilistic comparisons of measured D4 and D5 sediment residues to sediment chronic toxicity NOEC values for benthic species may still prove useful for risk evaluation of each material.Page 3 of 24 

### Materials and Methods

Sediment Field Residues - D4: For D4, data on sediment concentrations were taken from Norden (2005), NILU (2007), Boehmer and Gerhards (2003), Powell and Kozerski (2007), Powell (2012), and Wang et al. (2010); these data represent urban waterw ays, sediment downstream from municipal wastewater treatment plants (WWTPs), landf ills, marine systems, and sediment from industrial WWTPs. The data come from sites in the United States, Canada, Norway, Iceland, Sweden, Germany, the UK, Denmark, Finland, and Japan. Table 1 presents the D4 sediment field data, both in terms of mass of D4 per g-dry weight (dw) sediment and mass of chemical per g-organic carbon (OC). The highest concentrations of D4 appear to be associated with urban settings and industrial WWTPs, likely due to wastewater discharge of D4 present in personal care products and other applications. Some sediment samples contained no detectable residue of D4; in these cases, the limit of detection (LOD) is presented in parentheses. For samples in Table 1, such as the sediments taken from Lake Pepin, the measured concentrations of D4 in sediment were less than the method detection limit (MDL) but greater than the LOD; these low level D4 sediment concentrations were estimated and the data presented in Table 1. A cumulative frequency distribution of all measured D4 sediment concentrations (ng/g-dw) is presented in Figure 1, using a log-normal distribution described by the presented linear regression model; if no detectable D4 was noted in a sediment sample, the data were not included in the sediment frequency distribution. The 95 th 

### centile exposure concentration (i.e., the concentration that encompasses 95% of the measured D4sediment dw residues) may be calculated form a linear regression analysis of the lognormal distribution; for D4 in sediment, the 95 th 

### centile field residue is approximately 117 ng/g-dw. When the sediment residue samples have measured organic carbon levels, it is possible to produce a similar cumulative probability function for D4 sediment residues normalized to organic carbon content (ng/g-OC); these data are shown on Figure 2. For several of the sediment samples with measurable D4 residues (ng/g-dw), it was necessary to assume a sediment %OC level (i.e., 2.0%) to include these pertinent data in the D4 sediment residue OC distribution. As shown in Figure 2, the 95 th 

### centile sediment exposure concentration calculated fo r D4 from the log-normal linear regression model was 3850 ng/g-OC. The highest D4 sediment concentration (ng/g-dw) was observed by Powell and Kozeraki (2007) in Toronto harbor (Canada), with a single measured residue of 287 ng/g-dw or 13,667 ng/g-OC when normalized by the measured

OC content of 2.1%.

Sediment Field Residues – D5: For D5, the sediment residues examined in a probabilistic approach were taken from Norden (2005), NILU (2007), Powell et al. (2009), Powell (2010), Boehmer and Gerhards (2003), Powell and Kozerski (2007), Powell (2012) and Wang et al. (2010); these data represent urban waterways, sediment downstr eam from municipal wastewater treatment plants (WWTPs), landfills, marine systems, and sediment from industrial WWTPs. The data come from sites in the United States, Canada, Norway, Iceland, Sweden, Germany, the UK, Denmark, Finland, and Japan. Table 2 presents the D5 sediment field data, both in terms of mass of D5 per g-dw sediment and mass of chemical per g-OC; the highest D5 sediment residue was collected in a sediment sample taken from an industrial WWTP. Some sediment samples contained no detectable residue of D5; in these cases, the limit of detection (LOD) is presented in parentheses. A cumulative frequency distribution of the measured D5 sediment concentrations (ng/g-dw) is presented in Figure 3, using a log-normal distribution described by the presented linearPage 4 of 24 

### regression model. The 95 th 

### centile exposure concentration is approximately 1330 ng/gdw. Many of the sediment samples with measurable D5 residues have measured %OC levels and for other samples it was necessary to assume a sediment %OC level (i.e., 2.0%) to include these data in the D5 sediment residue OC distribution. As shown in Figure 4, the 95 th 

### centile sediment exposure concentration calculated for D5 from the log-normal linear regression model was 61, 500 ng/g-OC, with the highest, outlying D5 sediment residue taken in a sediment sample from an industrial WWTP.

Chronic Toxicity Data with benthic organisms: D4 and D5 In a best case scenario, probabilistic risk assessment involves a diverse range of species, in an attempt to simulate the taxanomic and functional diversity of aquatic ecosystems. The wide array of standardized pelagic test species (e.g., trout, bluegill, daphnia, etc.) is not replicated in the current freshwater sediment toxicity assays commonly used by OECD (Guidelines 218 and 225) and the US EPA (OPPTS 850.1735). The arthropods Chironomus tentans and Chironomus riparius are commonly used in chronic sediment exposures (OECD guideline 218); the amphipod Hyalella

azteca (EPA OPPTS 850.1735 ), and the oligochaete Lumbriculus variegatus (OECD guideline 225) may also be used for chronic sediment toxicity assays.

The sediment toxicity of D4 has been evaluated in chronic, 28-day assays with Chironomus riparius (Krueger et al., 2008a) and Lumbriculus variegatus (Picard, 2009a), and in a 14-day subchronic exposure with Chironomus tentans (McNamara, 1995; Kent et al., 1994); the pertinent no-observed effect concentration (NOEC) results, both in

terms of  g/g-dw and  g/g-OC, are presented in Table 3. The Chironomus tentans study (McNamara, 1995; Kent et al., 1994) produced three NOEC values based on low, medium, and high organic carbon sedi ments and mean NOEC and standard deviation values were calculated from these data. Two separate Lumbriculus variegates studies were conducted with D4 and work by Krueger et al. (2009a) with artificial sediment and non-synchronized worms did not produce a NOEC value. A subsequent study by Picard (2009a) on natural sediment and synchroni zed worms (as per OECD guideline 225)

resulted in a NOEC of 13  g/g-dw or 591  g/g-OC; these data were used in the D4 benthic invertebrate toxicity distribution. The results of a BSAF bioaccumulation study

with D4 and Lumbriculus variegates (Krueger et al., 2009b) are also noted in Table 3 as additional evidence pertaining to the chronic NOEC value observed with this species and sediment-spiked D4. The BSAF values observ ed for D4 in Lake Pepin invertebrates (7 and 19; Powell et al., 2009) were also used to estimate a range of D4 benthic invertebrate NOEC body burden levels in terms of  g/g-lipid; these data are also shown in Table 3, using an assumed worst-case BSAF for D4 of 20. In summary, the NOEC

values for Chironomus tentans (mean value), Chironomus riparius , and Lumbriculus variegatus were used to develop chronic sediment toxicity distributions for D4. From these distributions, 5 th 

### centile NOECs (i.e., the estimated chronic NOEC values for the

lowest theoretical 5 th 

### centile sensitive species) were extrapolated, consistent with Stephan et al. (1985), for comparison with D4 exposure data.

The sediment toxicity of D5 has been evaluated in chronic, 28-day assays with Hyalella

azteca (Picard, 2009b; Norwood et al., 2010) Chironomus riparius (Ransom and Putt, 2009b; Krueger et al., 2008b), and Lumbriculus variegatus (Krueger et al., 2007); the pertinent no-observed effect concentration (NOEC) results, both in terms of  g/g-dw and

 g/g-OC, are presented in Table 3. The Hyalella azteca testing produced three chronic NOEC values (Picard, 2009b; Norwood et al., 2010), Chironomus riparius studiesPage 5 of 24 

### (Ransom and Putt, 2009b; Krueger et al., 2008b) produced two NOEC values; mean

NOEC and standard deviation values were calculated from these data. The results of a BSAF bioaccumulation study with D5 and Lumbriculus variegates (Krueger et al., 2009c) are also noted in Table 3 as additional evidence pertaining to the chronic NOEC value observed with this species and sediment-spi ked D5. The BSAF values observed for D5 in Lake Pepin invertebrates (range of 1 to 5; Powell et al., 2009) were also used to estimate a range of D5 benthic invertebrate NOEC body burden levels in terms of  g/glipid; these data are also shown in Table 3, using an assumed worst-case BSAF value for D5 of 5. In summary, the NOEC values for Hyalella azteca

, Chironomus riparius (mean value), and Lumbriculus variegatus were used to develop chronic sediment toxicity distributions for D5. From these distributions, 5th centile NOECs were extrapolated, consistent with Stephan et al. (1985), for comparison with D5 exposure

data.

D4 and D5 biota residues – benthic invertebrate species Benthic invertebrate biota concentrations of D4 and D5 on a lipid basis were taken from field data on Lake Pepin (Powell et al., 2009) and Inner and Outer Oslofjord (Powell, 2010); the data are presented in Table 4. The data have been analyzed and sorted into separate cumulative probability distributions, which appear for D4 and D5 in Figures 5 and 6, respectively.

Powell et al. (2009) measured biota-sediment accumulation factors (BSAF) for D4 and D5 in Lake Pepin sediments and biota. The average BSAF values for benthic invertebrate species exposed to D4 ranged from 7 (mayfly nymphs) to 19 (midge larvae), while the average BSAF values for D5 ranged from 1 (mayfly nymphs) to 5 (midge larvae). These BSAF values were used in this analysis to estimate biota residues in benthic invertebrate species corresponding to D4 and D5 NOEC levels from sediment toxicology studies featuring measured sediment residues. The calculated D4 benthic invertebrate biota NOEC distribution is presented in Figure 5, where the D4 Lake Pepin BSAF value of ~20 was used to calculate the corresponding biota chronic NOEC levels. Similarly, the calculated D5 benthic invertebrate biota NOEC distribution is presented in Figure 6, where the D5 Lake Pepin BSAF value of ~5 was used to calculate the corresponding biota chronic NOEC levels.

Results and Discussion

Risk Assessment - D4: Figure 1 presents the D4 sediment field data in a cumulative probability distribution, along with a comparison to a cumulative probability distribution of the D4 NOEC values from the chronic sediment toxicity assays. The respective distributions indicate that there is no overlap between the 95 th 

### centile concentration for D4 in sediment (70 ng/gdw) and the calculated 5 th 

### centile D4 chronic NOEC of 4100 ng/g-dw. In probability theory, if two events, A and B, are independent, the probability (P) that they both occur is equal to the product of the probabilities of the two individual events:

(2)

Hence, the probability that a 95 th 

### centile exposure event will occur simultaneously with the presence of a sensitive 5 th 

### centile benthic invertebrate organism, is 0.25%; the likelihood this event will not occur is 99.75%. Even at this low probability event, a safetyPage 6 of 24 

### factor of ~60-fold exists separating the 95 th 

### centile D4 sediment exposure level and the

5 th 

### centile D4 chronic NOEC value in sediment.

If the D4 concentrations are expressed on an OC basis, as shown in Figure 2, the D4 residues for both exposure and chronic sediment toxicity similarly indicates an absence of overlap of unlikely events. The 95 th 

### centile concentration for D4 in sediment (3850 ng/g-OC) does not overlap with the calculated 5 th 

### centile D4 chronic NOEC of 85,700 ng/g-OC; a safety factor of 20-fold exists between the two metrics. It is noteworthy that all measured laboratory NOEC values for D4 are either roughly approximate to or in excess of the calculated OC solubility concentration for D4 (i.e., water solubility * K
oc 

### ),

9.3 x 10 5 

### ng/g-OC. This suggests that physical deposition of D4 in sediment OC may not chronically affect benthic invertebrate organisms. These findings are also consistent with the recent work of Redman et al. (2012), who found the data on D4 consistent with

a narcotic mode of action.

Finally, the D4 chronic sediment NOEC values may be expressed on a body burden basis (ng/g-lipid) using the worst-case field BSAF value of ~20 (Powell et al., 2009) and compared to field biota concentrations of D4 in benthic organisms (Powell et al., 2009; Powell, 2010); the two cumulative probability distributions are presented in Figure 5. The graph demonstrates the lack of over lap between exposure and chronic NOEC values, and even a low probability co-occurrence event (0.25%) retains a safety factor of approximately 300-fold.

In conclusion, the available sediment and biota field data on D4 indicate that the likelihood of concentrations of this material approaching the 5 th 

### centile chronic sediment NOEC for D4 with benthic invertebrate species is less than 0.25%, i.e., the probability of an absence of this event is >99.75%. This risk analysis indicates that current D4 sediment residues pose little to no risk to benthic species. Further research would be useful to explore the applicability of this relationship to other benthic species and other likely sites for cVMS in sediment.

Risk Assessment – D5: Figure 3 presents the D5 sediment field data in a cumulative probability distribution, along with a comparison to a cumulative probability distribution of the D5 NOEC values from the chronic sediment toxicity assays. The respective distributions indicate that there is no overlap between the 95 th 

### centile concentration for D5 in sediment (1330 ng/gdw) and the calculated 5 th 

### centile D5 chronic NOEC of 8360 ng/g-dw. The probability

that a 95 th 

### centile exposure event will occur simu ltaneously with the presence of a 5 th


### centile sensitive benthic invertebrate organism is low (0.25%), and a safety factor of ~6 exists separating these low probability events.

If the D5 concentrations are expressed on an OC basis, as shown in Figure 4, the D5 residues for both exposure and chronic sediment toxicity similarly indicates an absence of overlap of unlikely events. The 95 th 

### centile concentration for D5 in sediment (61,500 ng/g-OC) does not overlap with the calculated 5 th 

### centile D5 chronic NOEC of 380,000 ng/g-OC; a safety factor of 6-fold separat es these two unlikely co-events. It is noteworthy that all measured laboratory NOEC values for D5 are either roughly approximate to or in excess of the calculated OC solubility concentration for D5 (i.e., water solubility * Koc), 2.5 x 10 6 

### ng/g-OC. This suggests that physical deposition of D5 in sediment OC may not affect benthic invertebrate organisms. These findings are alsoPage 7 of 24 

### consistent with the recent work of Redman et al. (2012), who found the data on D5

consistent with a narcotic mode of action.

A final comparison of the D5 chronic sediment NOEC values may be expressed on a body burden basis (ng/g-lipid) using the worst-case field BSAF value of ~5 (Powell et al., 2009) and compared to field biota concentrations of D5 in benthic organisms (Powell et al., 2009; Powell, 2010); the two cumulative probability distributions are presented in Figure 6. The graph demonstrates the lack of overlap between exposure and chronic NOEC values, and even a low probability co-occurrence event (0.25%) retains a safety factor of approximately 3-fold.

Conclusions

In conclusion, the cVMS materials D4 and D5 are highly lipophilic (i.e., log K
ow 

### >6) and are therefore will predominantly reside in sediment when exposed to natural aquatic systems. The likelihood of sediment envir onmental exposure levels of D4 and D5 to reach chronic NOEC values with benthic invertebrate species may be assessed using probabilistic risk assessment techniques. For both compounds, there is an absence of overlap of sediment exposure concentrations (i.e., 95 th 

### centile exposure levels) with an interpolated, highly sensitive 5 th 

### centile benthic sediment NOEC trigger level, both with dry weight and organic carbon adjusted sediment levels. Field worst-case BSAF values of 20 and 5 were used for D4 and D5, respectively, for comparison of field collected benthic invertebrate body burden concentrations with calculated benthic chronic NOEC body burden concentrations, again using an interpolated, highly sensitive 5 th 

### centile NOEC trigger level for comparison; no overlap was calculated for either D4 or D5. This risk analysis indicates that existing D4 and D5 sediment residues pose virtually no quantifiable risk to benthic invertebrate species. Further research could explore the applicability of this relationship to other benthic species and also exposure the absence of apparent toxicological impact of D4 and D5 on benthic invertebrate organisms even at concentration levels exceeding their respec tive OC solubility values in sediment.Page 8 of 24 

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Norden. 2005. Siloxanes in the Nordic Environment (PDF Format, 1.6MB). TemaNord 2005:593. Nordic Council of Ministers, Copenhagen.

Norwood, W., et al. 2010. Decamethylcyclopentasiloxane (D5) spiked sediment: Bioaccumulation and toxicity to the benthic invertebrate. Environment Canada.

Available at: http://www.sehsc.com/presentations.asp

Pickard C. 2009a. D4-sediment-water Lumbriculus toxicity test using spiked natural sediment, following OECD Guideline 225. Unpublished report of Dow Corning #2009- I0000-61113, performed for CES.

Pickard C. 2009b. 28-day toxicity test exposing freshwater amphipods ( Hyalella azteca ) to D5 applied to sediment under static-renewal conditions following OPPTS DraftPage 10 of 24 

### Guideline 850.1735. Unpublished report of Dow Corning #2009-I0000-60662, performed

for CES.

Powell DE and Kozeraki GE. 2009. Cyclic methylsiloxane (cVMS) materials in surface sediment and cores from Lake Ontario. Unpublished report of Dow Corning Corporation

#2009-I0000-60886.

Powell DE and Woodburn KB. 2009. Trophic Dilution of Cyclic Volatile Methylsiloxane (cVMS) Materials in a Temperate Freshwater Lake. Dow Corning Corporation, HES Study No. 10771-108.

Powell DE. 2010. Bioaccumulation and trophic transfer of cyclic volatile methylsiloxane (cVMS) materials in aquatic marine food webs of the Inner and Outer Oslofjord, Norway. Dow Corning Corporation, HES study number 11060-108.

Powell DE. 2012. Sediment data from Tokyo Bay, Japan. Personal communication.

Qiao P, Gobas FAPC, and Farrell AP. 2000. Relative contributions of aqueous and dietary uptake of hydrophobic chemicals to the body burden in juvenile rainbow trout.
Arch Environ Contam Toxicol. 39:369–377.

Ransom RE and Putt AE. 2005. Decamethylcyclopentasiloxane (D5) - The full life-cycle toxicity test with midge ( Chironomus riparius ) under static conditions. Unpublished report of Dow Corning #2005-I0000-54997, performed for SEHSC.

Redman AD, Mihaich E, Woodburn K, Paquin P, Powell D, McGrath JA, and Di Toro DM. 2012. Tissue-based risk assessment of cyclic volatile methyl siloxanes. Environmental Toxicology and Chemistry, published on-line July 2012.

Schlabach M, Andersen MS, Green N, Schøyen M, and Kaj L. 2007. Siloxanes in the Environment of the Inner Oslofjord. NILU OR 27/2007. SPFO-report 986/2007, TA-no.

2269/2007.

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Stephan CE, Mount DE, Hansen DJ, Gentile JH, Chapman GA, and Brungs WA. 1985. Guidelines for deriving numerical national water quality criteria for the protection of aquatic organisms and their uses. PB85-227049. U.S. EPA, Washington, DC.

Thomann RV. 1989. Bioaccumulation model of organic chemical distribution in aquatic
food chains. Environ. Sci. Technol. 23: 699-707.

Wang D, Steer H, Young T, Tait T, Williams Z, Pacepavicius G, Ng T, Smyth SA, Kinsman L, Sverko E, Svoboda L, Fazal S, and Alaee M. 2010. Concentrations of cyclic volatile methylsiloxanes (cVMS) in various environmental media from Southern Ontario
and Southern Quebec. Environment Canada. Available at: http://www.sehsc.com/PDFs/Alaee%20Siloxane%20workshop%20May%202012.pdfPage 11 of 24 

### Weisbrod A, Woodburn K, Koelmans AB, Parkerton TF, McElroy A, Borga K. 20009. Evaluation of bioaccumulation using in vivo laboratory and field studies. IEAM 5: 598-

623.Page 12 of 24 

### Table 1. Collected field residue data on D4 in sediment.

Sediment D4 D4

Source Country Location Site %OC (dw) (ng/g

‐ dw) (ng/g

‐ OC)

Powell et al. 2009 USA L. Pepin, MN I.1 3.26% 1.5* 46

Powell et al. 2009 USA L. Pepin, MN I.2 3.31% 1.2 36

Powell et al. 2009 USA L. Pepin, MN I.3 3.22% 1.1 34

Powell et al. 2009 USA L. Pepin, MN I.4 3.07% 1.1 36

Powell et al. 2009 USA L. Pepin, MN I.5 2.89% 1.3 45

Powell et al. 2009 USA L. Pepin, MN II.1 3.43% 1.5 44

Powell et al. 2009 USA L. Pepin, MN II.2 3.57% 1.4 39

Powell et al. 2009 USA L. Pepin, MN II.3 3.50% 1.5 43

Powell et al. 2009 USA L. Pepin, MN II.4 3.41% 1.2 35

Powell et al. 2009 USA L. Pepin, MN II.5 2.50% 1 40

Powell et al. 2009 USA L. Pepin, MN III.1 3.73% 1.7 46

Powell et al. 2009 USA L. Pepin, MN III.2 3.67% 1.7 46

Powell et al. 2009 USA L. Pepin, MN III.3 3.61% 1.5 42

Powell et al. 2009 USA L. Pepin, MN III.4 3.73% 1.7 46

Powell et al. 2009 USA L. Pepin, MN III.5 3.74% 1.6 43

Powell et al. 2009 USA L. Pepin, MN IV.1 2.93% 1.5 51

Powell et al. 2009 USA L. Pepin, MN IV.2 3.91% 1.9 49

Powell et al. 2009 USA L. Pepin, MN IV.3 3.99% 1.8 45

Powell et al. 2009 USA L. Pepin, MN IV.4 4.17% 2.3 55

Powell et al. 2009 USA L. Pepin, MN IV.5 2.89% 1.4 48

Powell et al. 2009 USA L. Pepin, MN V.1 4.22% 2.0 47

Powell et al. 2009 USA L. Pepin, MN V.2 4.47% 2.2 49

Powell et al. 2009 USA L. Pepin, MN V.3 4.30% 2.1 49

Powell et al. 2009 USA L. Pepin, MN V.4 4.26% 2.3 54

Powell et al. 2009 USA L. Pepin, MN V.5 4.22% 2.2 52

Schlabach et al. 2007 Norway Inner Oslofjord Lysaker 2 <31

Schlabach et al. 2007 Norway Inner Oslofjord Lysaker 1 <33

Schlabach et al. 2007 Norway Inner Oslofjord Oslofj A1 <24

Schlabach et al. 2007 Norway Inner Oslofjord Oslofj A2 <23

Schlabach et al. 2007 Norway Inner Oslofjord Bekkelagsbassenget BB14 <4

Schlabach et al. 2007 Norway Inner Oslofjord Bekkelagsbassenget BB5 <38

Powell (2010) Norway Inner Oslofjord Assume 2% 86 4300

Powell (2010) Norway Inner Oslofjord Assume 2% 98 4900

Powell (2010) Norway Outer Oslofjord Assume 2% 45 2250

Powell (2010) Norway Outer Oslofjord Assume 2% 1 50

Bakke et al. (2008) Norway Barents Sea (N=1) Assume 2% 40 2000

Bakke et al. (2008) Norway Barents Sea (N=10) <MDL

Evenset et al. (2009) Norway Svalbard Liefdefjorden, N=2 <4

Evenset et al. (2009) Norway Svalbard Kongsfjorden, N=2 <3.3

Norden, 2005 Denmark Coast area Kattegat <3

Norden, 2005 Denmark Ores und, Lynetten <5

Norden, 2005 Denmark Roskiide, coast area Assume 2% 84 4200

Norden, 2005 Faroe Islands Kaldakfjordur <11

Norden, 2005 Finland Helsinki, vakai (old city bay) <20

Norden, 2005 Finland Espco coastal sea <20

Norden, 2005 Iceland Sediment <4

Norden, 2005 Iceland Sediment <3

Norden, 2005 Iceland Sediment <10

Norden, 2005 Iceland Sediment <7

Norden, 2005 Norway Bergsjoen <60

Norden, 2005 Norway Bergsjoen <65

Norden, 2005 Norway Rogden <50

Norden, 2005 Norway Rogden <50

Norden, 2005 Norway Vrengansundet <10

Norden, 2005 Norway Leangbukta <20

Norden, 2005 Norway Brodrene Sunde Verft <11

Norden, 2005 Sweden Nissan <2

Norden, 2005 Sweden Nissan <2

Norden, 2005 Sweden Nissan <3

Norden, 2005 Sweden Landsorfsdjupet 0

‐ 2 cm <30

Norden, 2005 Sweden Goffandsdjupet 0

‐ 2 cm <30

Norden, 2005 Sweden Riddarfjarden <9

Norden, 2005 Sweden Essingen <13Page 13 of 24 

### Table 1 (cont). Collected field residue data on D4 in sediment.

Sediment D4 D4

Source Country Location Site %OC (dw) (ng/g

‐ dw) (ng/g

‐ OC)

Powell and Kozerski, 2007 Canada Lake Ontario Kingston Basin (surface) 2.4% <5.8

Powell and Kozerski, 2007 Canada Lake Ontario Niagara basin (surface) 4.6% <5.8

Powell and Kozerski, 2007 Canada Lake Ontario Rochester basin A (surface) 4.4% <5.8

Powell and Kozerski, 2007 Canada Lake Ontario Rochester basin G (surface) 4.4% <5.8

Powell and Kozerski, 2007 Canada Lake Ontario Missasauga basin (surface) 5.1% <5.8

Powell and Kozerski, 2007 Canada Lake Ontario Toronto Harbor July 2006 (surface) 0.02 287 (N=1),

<LOD (N=5) 13667

Powell (2012) Japan Tokyo Bay Tokyo Bay A

‐ 4 3.21 7.17 224

Powell (2012) Japan Tokyo Bay Tokyo Bay A

‐ 5 3.56 11.34 318

Powell (2012) Japan Tokyo Bay Tokyo Bay A

‐ 6 3.55 7.26 205

Powell (2012) Japan Tokyo Bay Tokyo Bay A

‐ 7 2.94 4.25 144

Powell (2012) Japan Tokyo Bay Tokyo Bay B

‐ 4 3.83 6.39 167

Powell (2012) Japan Tokyo Bay Tokyo Bay B

‐ 5 4.05 8.86 219

Powell (2012) Japan Tokyo Bay Tokyo Bay B

‐ 6 3.91 17.45 446

Powell (2012) Japan Tokyo Bay Tokyo Bay B

‐ 7 3.78 25.63 678

Powell (2012) Japan Tokyo Bay Tokyo Bay C

‐ 4 3.46 2.2 6 4

Powell (2012) Japan Tokyo Bay Tokyo Bay C

‐ 5 3.52 0.9 4 27

Powell (2012) Japan Tokyo Bay Tokyo Bay C

‐ 6 3.36 1.1 33

Powell (2012) Japan Tokyo Bay Tokyo Bay D

‐ 2 1.66 0.45 27

Powell (2012) Japan Tokyo Bay Tokyo Bay D

‐ 3 3.32 2.2 66

Powell (2012) Japan Tokyo Bay Tokyo Bay D

‐ 4 3.43 0.8 23

Powell (2012) Japan Tokyo Bay Tokyo Bay E

‐ 2 0.42 0.43 102

Powell (2012) Japan Tokyo Bay Tokyo Bay E

‐ 3 0.59 0.82 140

Powell (2012) Japan Tokyo Bay Tokyo Bay E

‐ 4 0.56 0.23 41

Powell (2012) Japan Tokyo Bay Tokyo Bay F

‐ 1 1.43 0.96 67

Powell (2012) Japan Tokyo Bay Tokyo Bay F

‐ 2 0.32 0.3 93

Powell (2012) Japan Tokyo Bay Tokyo Bay F

‐ 3 0.24 0.15 64

Wang et al. (2010) Canada Site 1 Municipal WWTP Sediment Measured 49.1 3517

Wang et al. (2010) Canada Site 2 Municipal WWTP Sediment Measured 4.7 131

Wang et al. (2010) Canada Site 3 Municipal WWTP Sediment Measured 10.9 323

Wang et al. (2010) Canada Site 4 Municipal WWTP Sediment Measured 15 381

Wang et al. (2010) Canada Site 5 Municipal WWTP Sediment Measured 15 394

Wang et al. (2010) Canada Site 7 Municipal WWTP Sediment Measured 29.2 1518

Wang et al. (2010) Canada Site 8 Industrial WWTP Sediment Measured 14.9 5456

Wang et al. (2010) Canada Site 9 Municipal WWTP Sediment Measured 45.2 3865

Wang et al. (2010) Canada Site 10 Municipal WWTP Sediment Measured 11.7 258

Wang et al. (2010) Canada Site 11 Municipal WWTP Sediment Measured 36.9 2641

* In Lake Pepin, values are less than MDL (~2.3 ng/g

‐ dw) but greater than LOD, so estimated measured values are reported.Page 14 of 24 

### Table 2. Collected field residue levels for D5 in sediment.

Sediment D5 residues D5 residues Source Country Location Site %OC (dw) (ng/g-dw)* (ng/g-OC)

Powell et al. 2009 USA L. Pepin I.1 3.26% 132 4034

Powell et al. 2009 USA L. Pepin I.2 3.31% 120 3630

Powell et al. 2009 USA L. Pepin I.3 3.22% 117 3625

Powell et al. 2009 USA L. Pepin I.4 3.07% 108 3519

Powell et al. 2009 USA L. Pepin I.5 2.89% 89 3078

Powell et al. 2009 USA L. Pepin II.1 3.43% 145 4232

Powell et al. 2009 USA L. Pepin II.2 3.57% 121 3376

Powell et al. 2009 USA L. Pepin II.3 3.50% 119 3408

Powell et al. 2009 USA L. Pepin II.4 3.41% 96 2814

Powell et al. 2009 USA L. Pepin II.5 2.50% 53 2116

Powell et al. 2009 USA L. Pepin III.1 3.73% 126 3379

Powell et al. 2009 USA L. Pepin III.2 3.67% 125 3396

Powell et al. 2009 USA L. Pepin III.3 3.61% 134 3709

Powell et al. 2009 USA L. Pepin III.4 3.73% 145 3899

Powell et al. 2009 USA L. Pepin III.5 3.74% 122 3253

Powell et al. 2009 USA L. Pepin IV.1 2.93% 72 2473

Powell et al. 2009 USA L. Pepin IV.2 3.91% 146 3734

Powell et al. 2009 USA L. Pepin IV.3 3.99% 143 3586

Powell et al. 2009 USA L. Pepin IV.4 4.17% 153 3679

Powell et al. 2009 USA L. Pepin IV.5 2.89% 69 2388

Powell et al. 2009 USA L. Pepin V.1 4.22% 126 2989

Powell et al. 2009 USA L. Pepin V.2 4.47% 139 3099

Powell et al. 2009 USA L. Pepin V.3 4.30% 138 3213

Powell et al. 2009 USA L. Pepin V.4 4.26% 124 2913

Powell et al. 2009 USA L. Pepin V.5 4.22% 112 2664

Powell (2010) Norway Inner Oslofjord 2.84% 319 11239

Powell (2010) Norway Inner Oslofjord 2.78% 285 10245

Powell (2010) Norway Inner Oslofjord 2.52% 352 13952

Powell (2010) Norway Inner Oslofjord 2.51% 255 10147

Powell (2010) Norway Inner Oslofjord 2.69% 524 19494

Powell (2010) Norway Inner Oslofjord 2.49% 460 18462

Powell (2010) Norway Inner Oslofjord 2.81% 312 11103

Powell (2010) Norway Inner Oslofjord 2.94% 270 9170 Powell (2010) Norway Outer Oslofjord Assume 2% 525 26250 Powell (2010) Norway Outer Oslofjord Assume 2% 394 19700

CES, 2003 Germany Rhine river, Wiesbaden, km 523 D5 <3.0 ng/g-dw*

CES, 2003 Germany Rhine river, Koblenz, km 603 D5 <3.0 ng/g-dw

CES, 2003 Germany Rhine river, Leverkusen, km 712 D5 <3.0 ng/g-dw

CES, 2003 Germany Rhine river, Emmerich, km 874 D5 <3.0 ng/g-dw

CES, 2003 Germany Rhine river, Hollands Diep, km 1306 D5 <3.0 ng/g-dw

CES, 2003 UK, marine Las St Abbs Mid 5 Surface D5 <3.0 ng/g-dw

CES, 2003 UK, marine D5 <3.0 ng/g-dw

CES, 2003 UK, marine D5 <3.0 ng/g-dw

CES, 2003 UK, marine D5 <3.0 ng/g-dw

CES, 2003 UK, marine D5 <3.0 ng/g-dw

CES, 2003 UK, marine D5 <3.0 ng/g-dw

CES, 2003 UK, marine D5 <3.0 ng/g-dw

CES, 2003 UK, marine D5 <3.0 ng/g-dw

CES, 2003 UK, marine D5 <3.0 ng/g-dw

CES, 2003 Germany Rhine river, Koln, km 672 Assume 2% 6 300

CES, 2003 Germany, F W Rhine river, Karlsruhe ,

km 377 Assume 2% 12 600

CES, 2003 Germany Hall Dike Creek, 3.5 km

downstream from STP Assume 2% 36 1815

CES, 2003 UK, marine River Mersey Assume 2% 53 2650

CES, 2003 Germany Rhine river, Krefeld, km

761 Assume 2% 91 4550

CES, 2003 UK, marine Cardiff Bay Assume 2% 228 11400Page 15 of 24 

### Table 2 (cont.). Collected field residue data on D5 in sediment.

Sediment D5 residues D5 residues Source Country Location Site %TOC (dw) (ng/g-dw)* (ng/g-OC)

Schlabach et al. 2007 Norway Inner Oslofjord Lysaker 2 Assume 2% 93 4650

Schlabach et al. 2007 Norway Inner Oslofjord Lysaker 1 Assume 2% 200 10000

Schlabach et al. 2007 Norway Inner Oslofjord Oslofj A1 Assume 2% 250 12500

Schlabach et al. 2007 Norway Inner Oslofjord Oslofj A2 Assume 2% 280 14000

Schlabach et al. 2007 Norway Inner Oslofjord Bekkelagsbassenget BB14 Assume 2% 690 34500

Schlabach et al. 2007 Norway Inner Oslofjord Bekkelagsbassenget BB5 Assume 2% 920 46000

Norden, 2005 Faroe Isla n Kaldakfjordur D5 <5 ng/g-dw

Norden, 2005 Sweden Nissan D5 <1 ng/g-dw

Norden, 2005 Sweden Nissan D5 <1 ng/g-dw

Norden, 2005 Denmark Coast area Kattegat D5 <2 ng/g-dw

Norden, 2005 Denmark Oresund, Lynetten D5 <3 ng/g-dw

Norden, 2005 Sweden Nissan Assume 2% 1.8 90

Norden, 2005 Norway Vrengansundet D5 <5 ng/g-dw

Norden, 2005 Sweden Goffandsdjupet 0-2 cm D5 <10 ng/g-dw

Norden, 2005 Iceland Sediment Assume 2% 7.4 370

Norden, 2005 Iceland Sediment Assume 2% 7.6 380

Norden, 2005 Sweden Landsorfsdjupet 0-2 cm D5 <20 ng/g-dw

Norden, 2005 Norway Leangbukta Assume 2% 12 600

Norden, 2005 Norway Bergsjoen D5 <30 ng/g-dw

Norden, 2005 Norway Bergsjoen D5 <30 ng/g-dw

Norden, 2005 Norway Rogden D5 <30 ng/g-dw

Norden, 2005 Norway Rogden D5 <30 ng/g-dw

Norden, 2005 Finland Espco coastal sea Assume 2% 19 950

Norden, 2005 Iceland Sediment Assume 2% 19 950

Norden, 2005 Iceland Sediment Assume 2% 39 1950

Norden, 2005 Finland Helsinki, vakai (old city bay) Assume 2% 58 2900

Norden, 2005 Sweden Riddarfjarden Assume 2% 77 3850

Norden, 2005 Norway Brodrene Sunde Verft Assume 2% 96 4800

Norden, 2005 Sweden Essingen Assume 2% 130 6500

Norden, 2005 Denmark Roskiide, coast area Assume 2% 2000 100000
Wang et al. (2010) Canada Site 1 Municipal WWTP Sediment Measured 905.7 65560

Wang et al. (2010) Canada Site 2 Municipal WWTP Sediment Measured 22.5 630

Wang et al. (2010) Canada Site 3 Municipal WWTP Sediment Measured 63.1 1869 Wang et al. (2010) Canada Site 4 Municipal WWTP Sediment Measured 151.1 4676 Wang et al. (2010) Canada Site 5 Municipal WWTP Sediment Measured 43.9 559

Wang et al. (2010) Canada Site 7 Municipal WWTP Sediment Measured 1110.7 18035

Wang et al. (2010) Canada Site 8 Industrial WWTP Sediment Measured 5836 2184019

Wang et al. (2010) Canada Site 9 Municipal WWTP Sediment Measured 804 67474

Wang et al. (2010) Canada Site 10 Municipal WWTP Sediment Measured 70.8 1510

Wang et al. (2010) Canada Site 11 Municipal WWTP Sediment Measured 1556 119294Page 16 of 24 

### Table 2 (cont.). Collected field residue data on D5 in sediment.

Sediment D5 residues D5 residues Source Country Location Site %OC (dw) (ng/g-dw)* (ng/g-OC)

Powell (2012) Japan Tokyo Bay Tokyo Bay A-4 3.21 415.09 12941

Powell (2012) Japan Tokyo Bay Tokyo Bay A-5 3.56 392.71 11023

Powell (2012) Japan Tokyo Bay Tokyo Bay A-6 3.55 310.48 8750

Powell (2012) Japan Tokyo Bay Tokyo Bay A-7 2.94 212.42 7222

Powell (2012) Japan Tokyo Bay Tokyo Bay B-4 3.83 251.88 6569

Powell (2012) Japan Tokyo Bay Tokyo Bay B-5 4.05 261.60 6458

Powell (2012) Japan Tokyo Bay Tokyo Bay B-6 3.91 259.57 6630

Powell (2012) Japan Tokyo Bay Tokyo Bay B-7 3.78 277.31 7333

Powell (2012) Japan Tokyo Bay Tokyo Bay C-4 3.46 186.27 5377

Powell (2012) Japan Tokyo Bay Tokyo Bay C-5 3.52 161.17 4583

Powell (2012) Japan Tokyo Bay Tokyo Bay C-6 3.36 151.01 4500

Powell (2012) Japan Tokyo Bay Tokyo Bay D-2 1.66 22.25 1338

Powell (2012) Japan Tokyo Bay Tokyo Bay D-3 3.32 116.13 3495

Powell (2012) Japan Tokyo Bay Tokyo Bay D-4 3.43 99.01 2885

Powell (2012) Japan Tokyo Bay Tokyo Bay E-2 0.42 14.53 3448

Powell (2012) Japan Tokyo Bay Tokyo Bay E-3 0.59 23.05 3939

Powell (2012) Japan Tokyo Bay Tokyo Bay E-4 0.56 13.65 2436

Powell (2012) Japan Tokyo Bay Tokyo Bay F-1 1.43 46.90 3289

Powell (2012) Japan Tokyo Bay Tokyo Bay F-2 0.32 7.00 2167

Powell (2012) Japan Tokyo Bay Tokyo Bay F-3 0.24 6.27 2667

Powell and Kozerski, 2007 Canada Lake Ontario Kingston Basin (surface) 2.4% 7.90 329

Powell and Kozerski, 2007 Canada Lake Ontario Niagara basin (surface) 4.6% 13.70 298

Powell and Kozerski, 2007 Canada Lake Ontario Rochester basin A (surface) 4.4% 25.00 568

Powell and Kozerski, 2007 Canada Lake Ontario Rochester basin G (surface) 4.4% 16.20 368

Powell and Kozerski, 2007 Canada Lake Ontario Missasauga basin (surface) 5.1% 4.00 78

Powell and Kozerski, 2007 Canada Lake Ontario Toronto Harbor July 2006 (su 2.1% 790.10 37624

*D5 concentrations <method detection limit, which are shownPage 17 of 24Table 3. Chronic toxicity information on D4 and D5 with benthic invertebrate species in standardized testing regimes.BSAF = 20cVMSOrganismOECDGuidelineExposureDuration (d)Acute orChronicMostsensitiveendpointSpikedsediment? Conditions? %OCNOEC(μg/g-dw)NOEC(g/g-OC)NOEC(g/g- lipid) ReferenceD4Chironomus tentans218 14 Acute Survival >4.0 54 1350 2.70E+04 McNamara, 1991Chironomus tentans218 14 Acute Survival 1.5-3 120 8000 1.60E+05 McNamara, 1991Chironomus tentans218 14 Acute Survival <1 65 6500 1.30E+05 McNamara, 1991Mean = 80 5283 1.06E+05STDEV = 35 3488 6.98E+04Chironomus riparius28 ChronicEmerg. RatioArtificial sediment 4.1% 44 1073 2.15E+04 Krueger et al., 2008aLumbriculusvariegatus225 28 Chronic Surv/ReproArtificial sed, nonsync worms 2.4 <0.73 Krueger et al., 2009aLumbriculusvariegatus225 28 Chronic Surv/ReproNatural sed, syncworms 2.2 13 591 1.18E+04 Picard, 2009aLumbriculusvariegatusBSAF study 28 BSAF>4(highestdose) Krueger et al., 2009bBSAF= 5D5Hyalella aztecaOPPTS850.1735 28 Chronic Growth Yes Natural sediment 4.8 130 2708 13542 Picard, 2009Hyalella azteca28 Chronic Growth YesNatural - LakeErie 0.5 62 12400 62000 Norwood et al., 2010Hyalella azteca28 Chronic Growth YesNatural - LakeRestoule 11.0 641 5827 29136 Norwood et al., 2010Mean = 278 6979 34893STDEV = 316 4947 24737Chironomus ripariusOECD 218 28 Chronic Male develop. YesArtificial sed, withfood added dailyto water 2.0 69 3450 17250 Ranson and Putt,2005Chironomus ripariusOECD 218 28 Chronic Develop. Rate YesArtificial, 28-dfood ration spikedto sediment attest initiation 3.2 70 2188 10938 Krueger et al., 2008Mean = 70 2819 14094STDEV = 1 893 4464LumbriculusvariegatusOECD 225 28 Chronic Surv/Repro Yes Artificial sediment 3.7% 1272 34378 171892 Krueger et al., 2007LumbriculusvariegatusOPPTS850.173528 BSAF test YesWormreproductionapparent>336(highestdose)Krueger et al., 2009Page 18 of 24 

### Table 4. Monitored D4 and D5 residues in biota from Lake Pepin (MN, USA) and Oslofjord (Norway), ranked by D5 concentration (ng/g-lipid).

Location Species N %lipid D4(ng/glipid) STDEV(ng/glipid) D5 (ng/g

lipid) STDEV(ng/glipid)

Outer Oslofjord Mussell (sp. B) 3 0.81 ND* 537 50 Outer Oslofjord Mussell (sp. A) 3 1.38 14 2 1029 55

Outer Oslofjord Worms 1 (composite) 0.32 16 1254 L. Pepin Burrowing mayfly 2 (composite) 2.54 309 36 3895 341 Outer Oslofjord Sea urchin 3 0.32 154 17 4159 761 Inner Oslofjord Mussel (sp. B) 2 0.90 21 4 4468 277 Inner Oslofjord Blue mussel 5 0.81 18 3 5333 1303 L. Pepin Midge 5 (composite) 0.85 888 304 18114 8860 Inner Oslofjord Mussel (sp. A) 2 0.76 36 8 34137 18322

Inner Oslofjord Worms 1 (composite) 0.32 2687 172777

*ND = Not detectedPage 19 of 24 

### Figure 1. Probability distributions for D4 sediment concentrations (ng/g-dw) versus chronic toxicity NOEC values (ng/g-dw) for benthic invertebrate species.



## D4: Sediment Exposure Ranking versus Chronic NOEC Ranking

D4 Sediment Concentration (ng/g-dw) 10 -1 10 0 10 1 10 2 10 3 10 4 10 5%Centile Ranking (Probability)

1 10 30 50 70 90 99

Lumbriculus variegatus (28-d) Chironomus riparius (28-d) Chironomus tentans (14-d)

5 th centile ecotox NOEC = 4100 ng/g-dw 95 th centile exposure = 70 ng/g-dw

Difference of 60x Cummulative prob. 0.25% Exposure

Toxicity

N=62 intercept = -0.720 slope = 1.282

r 2 = 0.9350 intercept = -8.385 slope = 1.865

r 2 = 0.8553Page 20 of 24 

### Figure 2. Probability distributions for D4 sediment concentrations (ng/g-OC) versus chronic toxicity NOEC values (ng/g-OC) for benthic invertebrate species.

D4: Sediment Exposure Ranking vs. Chronic NOEC Ranking - OC Based



### D4 Sediment Concentration (ng/g-OC) 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8%Centile Rank

1 10 30 50 70 90 99

intercept = -8.186 slope = 1.326

r 2 = 0.9354

Lumbriculus variegatus
(28-d) N=62 intercept = -2.516 slope = 1.160

r 2 = 0.8207

95 th centile exposure concentration = 3850 ng/g-OC

5 th centile ecotox chronic NOEC = 8.57E4 ng/g-OC Difference of ~20x Cummulative prob. 0.25% Chironomus riparius (28-d) Chironomus tentans

(14-d), 3 values

D4 OC Solubility Limit = 9.3E5 ng/g-OCPage 21 of 24 

### Figure 3. Probability distributions for D5 sediment concentrations (ng/g-dw) versus chronic toxicity NOEC values (ng/g-dw) for benthic invertebrate species.



### D5: Sediment Exposure Ranking versus Chronic NOEC Values

D5 Sediment r esidues (ng/g-dw) 10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7%Centile ranking (Probability)

1 10 30 50 70 90 99 N=95 intercept = -2.985 slope = 1.48

r 2 = 0.9711

95 th centile = 1330 ng/g-dw

Chironomus riparius (28-d)

Mean of two studies

Hyalella azteca (28-d),

Mean of three values Lumbriculus variegatus (28-d) intercept = -5.830 slope = 1.067

r 2 = 0.9993 Chronic Toxicity Exposure

5 th centile ecotox NOEC = 8360 ng/g-dw Difference of ~6x Cummulative prob. 0.25% Industrial WWTP
SedimentPage 22 of 24 

### Figure 4. Probability distributions for D5 sediment concentrations (ng/g-OC) versus chronic toxicity NOEC values (ng/g-OC) for benthic invertebrate species.



### D5: Sediment Exposure Ranking vs. Chronic NOEC Ranking - OC-Based



## D5 Sediment Concentration (ng/g-OC) 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8%Centile ranking (Probability)

1 10 30 50 70 90 99 N=95 intercept = -5.083 slope = 1.405

r 2 = 0.9276

95 th centile conc = 6.15E04 ng/g-OC Exposure

Chronic Toxicity Intercept = -8.41 slope = 1.211

r 2 = 0.9754

5 th centile NOEC = 3.8 E05 ng/g-OC Difference is ~6x Collective probability

of 0.25%. Lumbriculus variegatus

Hyalella azteca (mean of three values)

Chironomus riparius (mean of two values) Industrial WWTP
Sediment

D5 OC solubility, 2.5E6 ng/g-OCPage 23 of 24 

### Figure 5. Probability distributions for D4 biota benthic invertebrate concentrations (ng/g-lipid) observed in Lake Pepin and Oslofjord organisms (Inner andOuter fjord) versus calculated biota chronic toxicity NOEC values (ng/glipid) for benthic invertebrate species (assumed D4 BSAF of 20; Powell et al., 2009).



### D4: Invertebrate Exposure Ranking vs. Chronic NOEC Ranking (BSAF assumed = 20 OC/lipid)

D4 Biota Concentration (ng/g-lipid) 10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9%Centile Ranking (Probability)

1 10 30 50 70 90 99

Lumbriculus variegatus
(28-d) Chironomus riparius (28-d) Chironomus tentans

(14-d), 3 values intercept = -9.911 slope = 1.326

r 2 = 0.93539

5 th centile ecotox NOEC= 1.72E6 ng/g-lipid intercept = -1.777 slope = 0.9120

r 2 = 0.8919

95 th centile exposure concentration = 5653 ng/g-lipid

Difference of 304x
Cummulative prob. 0.25% Blue mussel (Inner)

Mussel (B-Inner)

Mussel (A-Inner) Burrowing mayfly Midge Worms (Inner) Exposure Toxicity

Mussel (A-Outer) Worms (Outer) Sea urchin (Outer)Page 24 of 24 

### Figure 6. Probability distributions for D5 biota benthic invertebrate concentrations (ng/g-lipid) observed in Lake Pepin and Oslofjord organisms (Inner andOuter fjord) versus calculated biota chronic toxicity NOEC values (ng/glipid) for benthic invertebrate species (assumed D5 BSAF of 5; Powell et al., 2009).



## D5: Invertebrate Exposure Rank ing vs. Chronic NOEC Ranking (BSAF assumed = 5 OC/lipid)

D5 Biota Concentration (ng/g-lipid) 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9%Centile ranking (Probability)

1 10 30 50 70 90 99

intercept = -6.986 slope = 0.9308

r 2 = 0.7618

5 th centile chronic ecotox NOEC = 5.47 E5 ng/g-lipid intercept = -3.990 slope = 1.065

r 2 = 0.9394

95 th centile exposure concentration = 1.9 E5 ng/g-lipid

Differential of ~3x Cumulative prob. of 0.25% Mussel (B-Outer) Mussel (A-Outer) Worms (Outer) Burrowing mayfly Sea urchin (Outer) Mussell (B-Inner) Blue mussel (Inner) Midge Mussel (A-Inner) Worms (Inner)

Chironomus riparius (28-d),

Mean of two values Lumbriculus variegatus (28-d)

Hyalella azteca (28-d) Exposure Toxicity

# ATTACHMENT 4Page 1 of 2 

### Attachment 4

Powell, 2012

David E. Powell Dow Corning Corporation

Midland, MI 48686

Sediments were collected November 2011 as part of a preliminary monitoring assessment of
three cyclic volatile methylsiloxane (cVMS) ma terials in the aquatic ecosystem of Tokyo Bay
(SIAJ 2012). The three cVMS materials that were examined were Octamethylcyclotetrasiloxane
(D4; CAS No. 556-67-2), Decamethylcyclopentasiloxane (D5; CAS No. 541-02-6) and
Dodecamethylcyclohexasiloxane (D6; CAS No. 540-97- 6). Surface sediments (surface 1-cm of
sediment) were collected following a systematic probability sampling design based on a 5-km
mesh grid that extended across the bay and about 30 km seaward from the head to the foot of
the bay (defined study area of 500 km 2 

### ).

Concentrations of the three cVMS materials in surface sediments of Tokyo Bay were lognormally distributed over the study area, with concentrations decreasing with increasing distance from point-source discharges at the head of the bay. Generally, concentrations of the
three cVMS materials were less than or equal to the method detection limit in sediments
towards the foot of the bay. Because concentration gradients existed across the surface
sediments, the study area in Tokyo Bay was stratified based on the log-normally distributed
concentrations. Mean concentrations and standard deviations for the study area were calculated using methods for a stratified experimental design (Gilbert 1987). Results from the
systematic stratified design resulted in very pr ecise estimates of mean concentrations of the cVMS materials in surface sediment of the study area in Tokyo Bay (Table 1).

References:

Gilbert, R.O. 1987. Statistical methods for environmental pollution monitoring. Van Nostrand
Reinhold, New York, NY. 320 pp.

Silicone Industry Association of Japan (SIAJ). 2012. Pre-research for the interannual variability
survey (5-year monitoring survey ) of cyclic volatile methylsiloxanes in sediment and aquatic
biota in Tokyo Bay. Test result report. Environmental Control Center Co., Ltd. Kitakanto
Branch. 3-15-12 Honmachihigashi, Chuo-ku, Saitama-shi, Saitama, Japan, 338-0003.Page2of2Table 1. Mean uncensored concentrations of D4, D5, and D6 in surface sediments of the 500 km2study area in Tokyo Bay (SIAJ2012). Samples were collected by systematic sampling based on a fixed 5-km mesh grid. The study area was stratified into 4 stratabased on the log-normally distributed concentrations observed across surface sediments of the defined study area. Meanconcentrations and standard deviations were estimated using methods for a stratified design (Gilbert 1987).D4 D5 D6Measurement basis Mean SD Range Mean SD Range Mean SD RangeConcentration (ng/g dw) 5.9 1.1 0.20-26 160 11 6.3-420 32 2.3 2.2-72Concentration (ng/g ww) 1.6 0.25 0.15-6.1 45 2.8 4.8-110 9.1 0.58 1.7-14Concentration (ng/g TOC) 200 28 59-680 5400 190 1300-13000 1200 41 340-2300Analytical method detection limit (MDL) based on concentrations measured in wet sediments were:D4: 0.90 ng/g wwD5: 0.56 ng/g wwD6: 0.52 ng/g ww

# ATTACHMENT 5Page 1 of 5 

### Attachment 5

Kim, 2012

Major Factors Impacting Environmental Levels 1 

### of Siloxanes (D4/D5) at POTWs
Jaeshin Kim, August 1, 2012 Dow Corning Corporation

I. INTRODUCTION

Recently, the USEPA proposed major factors to develop a monitoring program design by a DOptimal Design process. Those factors are listed as below.

 Influent sources 1) Population size served by the POTW (High/Medium/Low)

 POTW characteristics 2) Treatment types (activated sludge, other secondary treatment types). Other secondary treatment types could include tri ckling filter, oxidation ditch or lagoons. 3) Total Suspended Solids (TSS) in effluent (high/low).

 Receiving body characteristics 4) Geography (North, South) 5) Receiving body flow (lake/ low flow stream/high flow river) 6) Organic content of receiving body (high/low)

Although these factors can impact the concentrations of D4 and D5 in the environment, a more systematic evaluation of the factors is needed using what is already known about the physicochemical properties and environmental monitoring of D4 and D5 to inform the appropriate path forward.

In order to employ a D-Optimal Design approach, a set of input parameters (or factors) is required which influence the output (i.e., environmental concentration of D4 and D5). Major factors are selected by the following three criteria (CR).

 CR 1: Major factors should be selected based on proper understanding or evidence of the input-output relationship.

 CR 2: The selected factors should be independent of each other.

 CR 3: The quantity of the factors should be known prior to the monitoring program.

To apply the criteria, it is critical to assess t he physicochemical properties of D4 and D5 (i.e.highly volatile, practically insoluble in water, preferential partitioning to organic phases, and nonbiodegradable).

II. APPLICATION OF THE CRITERIA

1 The “levels” is interpreted as “environmental concentrations” as implied in the section of Charge of the EPA

proposal and also mentioned during the conference call on Tuesday, July 24, 2012.Page 2 of 5 

### A. Criterion 1
Based on CR 1, the following three factors are not important factors for the output of D4/D5 environmental concentrations. The reasons are provided below.
1) Population size served by the POTW

When D4/D5 enters the wastewater stream from households, its total mass is proportional to population. However, their concentrations in the influent are dependent upon the flow rate and per capita usage rate in addition to population. Based on the USEPA NPDES database (total 10,415 POTWs 2 

### ), there is no significant correlation (r= – 0.001843, p< 0.05) between population and per capita wastewater flow rate as shown in the graph below. The per capita wastewater flow rate vary in a wide range as shown in the figure below (a median of 421 L/d-cap).

In a typical simple case where each person consumes a certain amount of D4/D5 and water per day, D4/D5 concentrations in the wastewater influent are expected to constant and independent of the population size. For example, when the average of per capita usage of D5 is 103 g/yr-cap, the down-the-drain rate is 9.5%, 3 

### and per capita water consumption is 200 L/g-cap, the total D5 concentration in influent would be 134 μg/L.

In reality, however, there are additional factors to be considered. Since a typical POTW may intake industrial wastewater and other source such as storm water, the overall incoming flow rate can be much higher than the water consumption by people, making

2 The database for 10,415 POTWs is also found in iSTREEM as an input data set and the POTWs discharge influents

to rivers or lakes.

3 These values were submitted to the Canada D5 Board of Review. 100 1000 10000 100000

10 100 1,000 10,000 100,000 1,000,000 10,000,000 Per capita

wastewater flow

rate (L/d

‐ cap)

Population served 

### Per capita wastewater flow rate (L/d

‐ cap) vs. Population Served

Correlation factor = ‐ 0.001843Page 3 of 5 

### influent D4/D5 concentrations increased or decreased. It is hard to estimate influent

D4/D5 concentrations without the information of the additional wastewater source.

At any rate, population alone cannot be an important factor for the D4/D5 environmental concentration. It is preferable that, among POTWs which do not receive industrial
wastewater, 4 

### facilities with high D4/D5 influent concentrations should be selected for the monitoring program. Since it is agreeable in general that the removal efficiency for a particular POTW type is similar, monitoring POTWs with low D4/D5 influent concentrations is not necessary. Since it is typically hard to measure low D4/D5 concentrations (e.g., <3 μg/L in wastewater effluent), we can reasonably predict the effluent concentration based on the removal efficiency of the same treatment type. In summary, instead of population as a major input variable , influent D4/D5 concentration at a high level can be used as an input constant

.

2) Geography

The factor of geography was considered because volatilization and solubility of D4 and D5 are dependent on temperature during wastewater treatment. The USEPA thinks that partition of D4/D5 to air would decrease as temperature decreases. Although this thought is not wrong, one should consider that the air-water partition coefficient (K AW 

### ) is VERY high over a typical range of wastewater treatment (e.g., 10 ~ 20 °C). This thermodynamic factor is therefore not a significant factor for the actual volatilization of D4/D5 during wastewater treatment. Rather the diffusive transport at the air-water interface is the rate determining step. In this regards, the volatilization/aeration rate at active sludge process is the most important to determine the removal of D4/D5 to the air compartment rather than the change of K AW 

### values.
In case of lagoon systems where no additi onal aeration is supplied, the overall volatilization can be more affected by the wind speed, which affects the transport of the chemical at the interface more than the water temperature (which affects diffusivity at the air-water interface). Even under a calm conditi on, the overall effect of temperature on effluent concentration is expected to be small (e.g., <±20%) with additional consideration of partition to suspended solid at different temperatures.
Thus, the factor of geography seems to be a minor factor for D4/D5 environmental

concentrations.

3) Receiving body flow (lake/ low flow stream/high flow river)
D4/D5 concentration in the receiving water body is influenced not only by the stream flow but also by the discharge flow rate from POTWs. Thus, the dilution factor is a better factor than the receiving body flow rate.
We propose to use dilution factor as an input variable and select POTWs with low and high values of dilution factor.

4 This can be done by excluding large wastewater flow rates per capita and/or only including small wastewater flow

rates. Note that the minimum wastewater flow is 189 L/d

‐ cap based on the NPDES database.Page 4 of 5 

### B. Criterion 2

The second criterion (CR 2) describes that the selected factors should be independent. According to this criterion, the two factors of treatment types and effluent TSS concentration

should be reconsidered due to the reason as follows.
Total Suspended Solids (TSS) is an operational performance parameter required for permit compliance by POTWs. Because of this, TSS is already constrained. Further, it is an indicator of plant performance along with ammonia and BOD levels which are typically used collectively to know if a monitored plant is oper ating properly. This is a key factor in eventual site selection. High TSS is normally associated with plant upsets following rain events. However, baseline TSS levels are treatment-type dependent (these are also reflected in POTW NPDES permits) such that a “low” level of TSS concentration in lagoon effluent is a “high” level of TSS concentration in activated sludge effluent.

In addition to effluent TSS concentration, the D4/D5 concentration in the TSS phase is also important. In most cases, the D4/D5 concentration in the TSS phase is strongly dependent on the degree of aeration during the wastewater treatment because D4/D5 removed by aeration is predominantly from the solid phase. The solid phase concentration is expected to decrease in the order that follows, for example: lagoons with no aeration > oxidation ditch > trickling filter > activated sludge with surface aeration > activated sludge with bubble aeration. It should be noted that the mass fraction in the water phase is much smaller than the mass fraction in the solid phase in the influent.
Since we can establish a general trend that TSS effluent concentration and solid phase D4/D5 concentration are dependent on treatment types, we propose to combine the two important factors of effluent TSS concentration and solid phase D4/D5 concentration into a single factor of treatment type with three levels such as low, medium and high for TSS concentration and solid phase D4/D5 concentration.

C. Criterion 3
Since the selection of monitoring POTW sites is the immediate goal, the input monitoring parameter should be also known prior to execution of the monitoring program. In this regard, the factor of organic content of receiving body is in question. Unless there is accurate database of the organic content for all the receiving water bodies near POTWs, it is impossible to locate sites with low or high organic content. Organic content is also highly dependent on season and subjected to watershed factors such as extent of extent of riparian zone. We assume that the monitoring program will measure the organic contents and use the information to support the concentration of D4/D5 in the sediment. In other words, the factor will be evaluated after, not before, the monitoring program. Thus, theorganic content of the receiving water body cannot be used as a major factor for a DOptimal procedure unless there is a well-established database for the item.
In addition, there is a concern about the factor of effluent TSS concentration. In general, the variation of reported effluent TSS concentration varies significantly depending on operation parameters at each site and even the way of sampling. Monitoring at limited times might induce a false information on the performance of a POTW. For example, the actual TSS concentration might be relatively high from an activated sludge at a particular sample time site although its mean concentration is low. For this reason, it is much safer to use D4/D5 concentration in the TSS phase as a major factor and to measure TSS concentration to support the overall effluent concentration. Monitoring of TSS during sampling efforts will be a key variable to assess as part of the larger effort.Page 5 of 5 

### III. SUMMARY & SUGGESTIONS
We have applied three criteria to evaluate six major factors proposed by the USEPA. All the factors of the USPEA are incomplete to be used as inputs for a D-Optimal procedure. Instead, we proposed a new set of input factors as listed below.

 Input variables 1) Treatment type (3 levels: low/medium/high for TSS concentration and solid phase D4/D5 concentration): for example, a. Low: activated sludge with bubble aeration (i.e., low TSS concentration and solid phase D4/D5 concentration) b. Medium: a trickling filter c. High: a lagoon without aeration (i.e., high TSS concentration and solid phase D4/D5 concentration) 2) Dilution factor (2 levels: low/high)

 Input constant 3) Predicted influent total concentration of D4/D5: only high concentration is needed. We can estimate effluent concentration from the same treatment type assuming the removal efficiency is same for the same type.

Thus, all six combinations (3×2, the full factorial) are suggested according to the new proposal. The total number could be reduced by a D-Optimal procedure (possible to three). The monitoring program will measure actual D4/D5 concentration in water and TSS of effluent and receiving water, TSS concentration in effluent, and organic content in sediment of receiving water body.

# ATTACHMENT 6Page 1 of 2 

### Attachment 6

MoE Analysis

Margin of error expresses the amount of random sampling error in the estimation of a parameter, such as mean concentration. As depicted in the figure and table below, the margin of error is one-half the width of the 95% confidence interval about the mean (i.e., the 95% confidence limit) expressed on a percentage basis. For example, the margin of error for a mean concentration based on n=5 samples collected from a population having an estimated CV= 20%

is ±24.8%. If the mean concentration was 100 then the 95% confidence interval about the mean would be 75.2 to 124.8. Similarly, the margin of error for a mean concentration based on n=5 samples collected from a population having an estimated CV= 50% is ±62.1%, giving a 95%

confidence interval about the mean of 37.9 to 162.1.

Total variability across a population is a function of temporal and spatial variability. For the cVMS materials, spatial variability is the principle consideration for environmental parameters (i.e., concentrations in sediment and biota) whereas temporal variability is the principle consideration for WWTP parameters (i.e., concentrations in influent, effluent, and biosolids). Within a defined system such as Lake Pepin the CV for concentrations of D5 in sediment ranges from about 15% (for concentrations expressed on the basis of organic carbon) to 25% (for concentrations expressed on the basis of wet weight), based on simple random sampling. The CV for concentrations of D5 in Tokyo Bay sediments (collected following a systematic probability sampling design) ranges from about 3.5% (for concentrations expressed on the basis of organic carbon) to 6.9% (for concentrations expressed on the basis of wet weight or dry weight).

Temporal variability associated with concentrations in a WWTP is not known, but could be estimated from the available data. I suspect that the CV for concentrations in influent are >100% over a 24-h period. In contrast, CVs for concentrations in effluent and biosolids will be much lower because concentrations in these matrices are integrated over the retention time

within the WWTP.Page 2 of 2 10 9 8 7 6 5 4 3 2 1 100

80

60

40

20

0

Number of samplesMargin of error (%) CV=10%

CV=20%

CV=30%

CV=40%

CV=50% 

### Margin of Error Power Curves

Margin of Error (%)

Number of Samples CV=10% CV=20% CV=30% CV=40% CV=50%

2 89.8 179.7 269.5 269.5 449.2

3 24.8 49.7 74.5 74.5 124.2

4 15.9 31.8 47.7 47.7 79.6

5 12.4 24.8 37.3 37.3 62.1

6 10.5 21.0 31.5 31.5 52.5

7 9.2 18.5 27.7 27.7 46.2

8 8.4 16.7 25.1 25.1 41.8

9 7.7 15.4 23.1 23.1 38.4

10 7.2 14.3 21.5 21.5 35.8