NMP Review

– Response to Comments, Attachment 1

Responses to Comments Thomas A rmstrong , Relevant Comments: Reviewer Comment : Dr. Armstrong ’s comments cover approximately 4 pages

. Selected relevant comments have been excerpted as follows :
The error dramatically shifts the shape of the Concentration vers us Time series for the scenarios so that maximum concentrations generally (but not always) occur after the consumer leaves the work zone. This means the correct personal exposure profiles will be dramatically lower than the erroneous results given in the c urrent NMP draft. The peak concentrations are also significantly affected, but perhaps not quite as dramatically.

The EPA Draft Risk Assessment for NMP uses an incorrect value for the first order exponential decay constant (K1) of 10 per hour (0.17 per mi nute) for NMP. Since this may be difficult to believe, this commentary goes into considerable detail to demonstrate the error is real and has significant impact in the Draft NMP RA. The second exponential decay constant (K2) for NMP appears approximately a ppropriate. The K1 of 10 per hour is the same value used for DCM in the Draft DCM RA. This K1 is an appropriate value for DCM, but is very erroneous for NMP. The vapor pressures, a prime determinant of K’s, are vastly different. This K1 is a parameter erro r used in the NMP modeling for Scenarios 1 to 8, and is unfortunately not just a typographical error in the list of parameters in the tables. The “downstream” work on NMP Margins of Exposure and other subsequent analyses using the results of Scenario 1 to 8 are then also significantly in error and will need to be redone.

Comparative Modeling Results to Demonstrate the K1 Error. One of the first indications of the error arises from Figure D

- 5 in the NMP Draft Risk Assessment (see Item 1 below). Another in dication is that a K1 of 10 per hour (0.17 per minute) means approximately 17 percent of the initial NMP mass evaporates in the first minute, and 17 percent of the amount remaining each minute thereafter in an exponentially decreasing manner. This 17 perce nt in the first minute is obviously not correct and not plausible given NMP’s low vapor pressure. Several scenarios from the NMP draft RA are examined to illustrate the effect of the error, but the following is not a complete examination of all the modeled NMP scenarios. Rather, the intent is to demonstrate how the wrong K1 of 10 per hour and a more appropriate K1 of 0.3 per hour alter the peak concentration and concentration versus time results. Note 0.3 per hour is an estimate and a proper analysis of Fig ure D

- 5 may lead to a slightly different value.

1. The exposure chamber (MRI, cited as EPA 1994b) results cannot be replicated with the incorrect K1 of 10 per hour (0.17 per minute). They can be adequately replicated with a K1 of 0.3 per hour (0.005 per min ute), which is much more a reasonable value for a low vapor pressure material like NMP. See attachment, figures in items 1, 2 and 3. 2. The MCCEM bathtub refinishing scenario results for NMP can be approximately replicated using the incorrect K1 of 10 per ho ur (0.17 per minute). Using the more appropriate K1 value of 0.3 per hour (0.005 per minute), the results are dramatically lower and do not approach saturation at Cmax. See figures in attachment, items 4 to 6. This is much more in line with expectations fo r NMP based on the chamber data and other real world data.

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3. As an additional spot check, an attempt to recreate the results for Scenario 1 is included in the attachment. Note that use of the INCORRECT K1 of 10 per hour reasonably but not exactly compares to the reported MCCEM results. If a more appropriate value of 0.3 per hours is used for K1, the results are dramatically different, for Cpeak as well as the shape of the C vs. t curve.
ATTACHMENTS NMP Modeling Issues 1. Figure D

- 5 and Table D

- 3 from page 101 of draft NMP Risk Assessment. These are stated as the results of fitting exponentials to the measured chamber concentration on the y axis. The Cmax is approximately 65 PPM or 263 mg/M 3 since 1 ppm = 4.06 mg/M 3

. These results CANNOT be as they are with a K 1 of 10 per hour.

This first order rate constant (K1) of 10 per hour cannot be from the fit to the data in the graph above. A K1 of around 0.3 per hour is consistent with the curve fit to the data. See the results shown in Item 2 and 3 below. 2. IH Mod Well Mixed Room Model with Exponentially Decreasing Emission Rate, attempt to replicate chamber results but using INCORRECT first order decay constant for NMP of 10 per hour = 0.17 per minute

. Parameters for the NMP quantity and chamber conditions as given in US EPA (1994b). Consumer exposure to paint stripper solvents. US Environmental Protection Agency, Washington, DC, but converted to PER MINUTE. Note Cmax of 248 mg/m 3 at 20 minutes. Note this is a version of a predecessor to IH Mod. The revised spreadsheet considers the second exponential mass and second exponential constant of 0.05 per hour = 0.00083 per minute.

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– Response to Comments, Attachment 1

Response : This comment generally can be summarized as follows : “Dr. Armstrong believes that: (1) The parameters reported in the NMP Risk Assessmen t document, Table D

- 3 are incorrect and CANNOT be used to obtain the results shown in Figure D

- 5; and (2) The k value for the first exponential is unrealistically high.

Based on the comments, it is clear that the reviewer does not understand the modeling al gorithms used in this study. For this reason, a reasonably thorough treatise of the double exponential and the incremental source models that are used to represent continuous

- application scenarios are provided in Attachment 1. A summary of the different ap proaches is presented graphically in Figure 1. The time series labeled “Armstrong’s version of the f it in the NMP risk assessment ” compared to th e time series labeled “The fit in the NMP Risk Assessment document” clearly demonstrates the misunderstanding. The reviewer mistakenly thinks that the NMP study applied a double exponential that does not r eflect continuous product application

. T he construct that the reviewer applied is appropriate only for a product where all product mass is instantaneously availab le, as in the case of a room deodorizer placed in a residence. This construct does not apply to a product such as paint stripper, where the applied mass steadily increases over time throughout the application period. The construct that OPPT applied has bee n termed an incremental source model and is used to mathematically represent a constant application rate over time coupled with an emission rate for each instantaneously applied segment that declines exponentially over time.

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– Response to Comments, Attachment 1

Clearly, if a double exponentia l without continuous application w ere used , t he n the model would

over

- predict the early emissions

. However, as noted above, this is not the model that OPPT used

. As is proven by the results shown in Figure 1 for the incremental model (the fit in the NMP Ri sk Assessment document

, which exactly matches our prediction obtained from MCCEM’s incremental source model ), the parameters given in Table D

- 3 CAN be used to obtain the results shown in Figure D

- 5.

Figure 1. Comparison of Al ternative Modeling A lgorithms Applied to the MRI NMP Data.

The question of an appropriate k value for the first exponential and the suggestion that it is unrealistically high are both due to a misunderstanding of the model algorithms. As discussed in Atta chment 1, t he two exponentials are used to represent different mass

- transfer processes with different rates. In the case of paint strippers, surface area is created during the application of the product. The first exponential is intended to represent the v olatilization of molecules (i.e

. , DCM or NMP) within a few μm of the surface during and shortly after the active application period. The second exponential would represent the diffusion and volatilization of molecules in the bulk applied product. The perce nt of applied mass emitted

– a result of our "best visual fit" to the chamber data

– was 26% of the applied mass being emitted, with 2% allocated to the 1 st exponential and 24% allocated to the 2 nd . While a k value as high as 10 may seem counterintuitive f or a relatively nonvolatile chemical such as NMP, it is perfectly legitimate when used in this context . More specifically , the mass applied to the first exponential (the exponential with the “fast” rate constant , in this case 10/hr ) is 2% of the total appl ied NMP, representing the limited mass of molecules available for rapid volatilization. The second

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– Response to Comments, Attachment 1

exponential (with the lower rate constant, in this case 0.05/hr) releases 24% of the applied NMP mass, representing the slower emissions.

Also

, as discussed in Attachment 1, the incremental form of the double exponential (which OPPT applied for predictive purposes) wa s used to distribute the emissions to account for the continuous application of paint stripper over each application event . The continuous appli cation spreads the rise out over the duration of the application by considering the time of application as the starting time of the exponential representation ; therefore

, the use of parameters to reflect the application rate, the duration of the applicatio n, and the percent of applied mass that is released impacts the slope of the concentration rise as well as the slope of the tail of the exponentials

. The 2% of applied mass attributed to the first exponential plays a significant role in properly representi ng the initial rise , while the 24% attributed to second exponential plays a significant role in properly representing the tail. By fitting a double exponential without continuous application

, as the reviewer did, the resulting exponentials necessarily will have an artificially lower rate constant to account for both the actual rate of decline as well as the delay due to the duration of the application. The final conclusion should be drawn from the fit of the model to the measurement data

. As can be seen fro m the MCCEM prediction in Figure 1 (which overlays the visual fit to the data), the structure of the data suggest s that the incremental version of the double exponential model is best capable of representing both the rise and the decay portions of the meas ured data. The help provided by MCCEM for emission models makes this distinction clear:
A single

- exponential source is one with an emission rate that declines exponentially over time as the reservoir of emitting material is progressively diminished

. This source model is most appropriate for emitting materials or furnishings that are placed indoors, rather than consumer products that are applied in some fashion

. The required inputs are the initial emission rate and the first

- order rate constant that describ es the exponential decline in the emission rate over time. A source of this type is "placed in" and "removed from" the house in accordance with the start and end times specified by the user. Consumer products that are applied to surfaces (e.g., architectur al coatings, all

- purpose cleaners) are best represented by the incremental source model

. This model assumes a constant application rate over time, coupled with an emission rate for each instantaneously applied segment that declines exponentially

. The mathe matical expression for the total emission rate resulting from the combination of constant application rate and exponential emission rate for each applied segment has been developed by Evans (1994). Depending on the type of applied product, either one or tw o exponential expressions may be needed to characterize the declining emission rate

. As described by Wilkes et al. (1996), for a product such as latex paint the first exponential typically governs the early phase of emissions due to evaporation, whereas th e second tends to dominate the later phase of emissions due to diffusion through the surface and/or applied product

. Although the first exponential is the primary determinant of the peak concentration (which usually occurs within or soon after the applicat ion period), the second exponential typically accounts for a much larger fraction of the total emitted

mass

. The user must specify the fraction of emitted mass associated with the first exponential and the rate constant for emissions decline for each of th e two exponentials

. At the user's option, one of the exponentials can be "turned off" by specifying either zero or 100 percent as the fraction of mass associated with the first exponential.

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– Response to Comments, Attachment 1

Unfortunately, the current version of MCCEM is not compatible with Window s 7/8. Consequently XP computers are retained for the purpose of running models that have not yet been ported to Windows 7/8, recognizing that not all modelers have this capability.

Reviewer Comment : Thomas Armstrong’ s Comment 4 on Page 16:
4. N MP Scenarios 1 to 8 evaluated with MCCEM use a double exponential model (Eq. D

- 5 page 96) with the two rate constants (K1 and K2) derived from fit to the MRI chamber data (EPA 1994b)

. Given good data from the MRI study, this gives an adequate basis for de termining the rate of NMP emission to air, under reasonably similar conditions. However, NMP will have a reduced vaporization rate at low air velocities. One source to support this is Matthews 1989 cited in DCM RA for other reasons. In industrial hygiene modeling, this has been called “back pressure” and especially for short duration activities with limited and low velocity airflow, a thin saturated vapor layer above the evaporating film suppresses further evaporation and saturation concentrations exist on ly very close to the film surface. This then disperses with the air flow, and the general space concentration rises, reaching a fraction of saturation that depends on the source NMP generation and dispersion, ventilation and volume. Failure to consider thi s may lead to significant overestimation of the NMP concentration in air. This may be handled better via different modeling equations than the ones used in MCCEM. A well

- mixed (single chamber) equation that provides for this is given in Chapter 5 of Keil, C. B., Ed. (2000). Mathematical Models for Estimating Occupational Exposure to Chemicals. Fairfax, VA, AIHA Press. Other forms of the model exist, including in a freeware spreadsheet [IH Mod, available at: http://ss4m.aiha.org/insideraiha/volunteergroups/EASC/Pages/EASCTopics.aspx While very difficult to determine without actual experimental data, the thin film stratification of NMP may be significant in a restricte d space and a concave surface with limited effective air velocity over the surface, such as a bathtub. At low air velocities, there is little turbulence to drive dispersion. These considerations place significant doubt on the application of the MRI Ks for the bathtub stripping scenario. This could be verified with some simple laboratory scale experiments.

Response: It is certainly likely that the off

- gassing rate is affected by turbulence. However, there are no readily available data to evaluate this asser tion. As the commenter suggests, investigating this potential impact would likely require some field and/or laboratory studies

. In addition, or
p erhaps alternatively, this investigation would require some sophisticated CFD modeling

. In deciding upon a cou rse of action to address this comment, the following factors should be considered:

1. Is it possible that these boundary layer factors significantly impact exposure? This leads to a discussion of the likelihood of a significant reduction in mass

- transfer and whether the predicted concentrations are near a risk

- derived critical concentration. If the concentrations are 10

- fold different from the critical concentration, this quite possibly is irrelevant. On the other hand, if the concentrations are close to the c ritical concentration,

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– Response to Comments, Attachment 1

then a preliminary estimate on the possible impact of this factor would be useful in determining the importance of this potential impact.

2. The exposure assessment was conducted under a set of realistic conditions, including ventilatio n assumptions. The source cloud assumption is based on estimates of room airflow velocities. The higher this velocity, the more the boundary layer is disturbed, enhancing mixing. If the surface velocities are expected to be low

, such that a boundary layer inhibits mass transfer and tend s to lower air concentrations, this would impl y that the source

- cloud ventilation would also be lower, tending to increase air concentrations.

3. During the brush

- on and scraping activities ( which based on label instructions wou ld occupy a majority of the time during any paint stripping activity ) the user is actively creating turbulence at the surface.

4. The uncertainty in many of the other parameters, including but not limited to product use behavior and ventilation rate near the source, may very well dwarf the uncertainty due to the potential impacts of low air velocities.

5. Per model predictions, the source cloud is at the saturation concentration during most of the stripping . Therefore

, a small decrease in the mass

- transfer rate likely w ould delay reaching the saturation concentration but w ould have a negligible impact after saturation is reached.
Given all these factors and the uncertainties noted above, the relative impact of application to a concave or curved surface likely is minor.

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– Response to Comments, Attachment 1



### Attachment 1: Description of Model Algorithms Double Exponential and Continuous Application

1. The Double Exponential Algorithm The double exponential empirical model is used to represent the emissions during application and the waiting period. Th e double exponential is the sum of two exponentials as given by equation A1

- 1. The two exponentials are used to represent different mass

- transfer processes with different rates. In the case of paint strippers, surface area is created during the application of the product. The first exponential is intended to represent the volatilization of molecules (i.e., DCM or NMP) within a few μm of the surface during and shortly after the active application period. The second exponential would represent the diffusion a nd volatilization of molecules in the bulk applied product.



### S t E e E e k t k t 

### ( ) = + − − 01 02 1 2 (A1

- 1)
where: S(t) = Source strength as a function of time (mass/time) E 01 = Initial emission rate for first exponential (mass/time) E 02 = Initial emission rate for seco nd exponential (mass/time) k 1 = First

- order rate constant for first exponential (time

- 1 ) k

2 = First

- order rate constant for second exponential (time

- 1 )
t = Time.

Integrating the mass balance equation for the one

- compartment case, using Equation A1

- 1 for t he source term and assuming an initial concentration of zero, gives Equation A1

- 2:

C t E

V Q
V k e e E

V Q
V k e e k t Q
V t k t Q
V t ( ) =

−     

 −     

 +

−     

 −     



− −

− − 01

1 02

2 1 2 (A1

- 2)

2. General Equation for Emissions from Continuous Application The double exponential equation is applicable to a short

- term product application. For the case of a brush

- or spray

- applied product, such as painting or paint stripping, the application occurs gradually by application in relatively continuous strips across the surface being treated. To represent the emissions and concentrations during a continuous application event, the application can be characterized as short

- duration application segments, with each segment represented by the double exponential algorithm, as shown in Figure A1

- 1. The sum of the segment

- specific exponentials is then fit ted to the measured data using a sum

- of

- squared

- difference s approach.

A single exponential cannot properly represent the rise and tail of the emissions and resulting concentrations because of the continuous nature of the product use. The continuous appli cation spreads the rise out over the duration of the application, and therefore the application rate and duration impact the slope of the concentration rise as well as the slope of the tail of the exponentials. An illustrative demonstration of this effect is shown in Figure A1

- 2, which compares the fitted sum of exponentials to a double exponential fit to the same data set. The single exponential does not capture the significant features of the dataset, falling short of the

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– Response to Comments, Attachment 1

peak and then falling off too rap idly after the end of the application period. Furthermore, the parameters of the double exponential will change if the length of the application period changes. The application period is one of the parameters of the continuous application algorithms.

F igure A1

- 1. Basis for Continuous Application Algorithm.

Figure A1

- 2. Comparison of a Double Exponential Fit to the Continuous Application Algorithm.

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– Response to Comments, Attachment 1

Modeling the emissions of the double exponential source term for an ongoing continuous application event was presented in a paper by Evans (1994). 1 This algorithm can be used in a predictive model to represent the source strength as a function of time for the combination of (1) continuous application of a product, such as paint or paint stripper, at an essentially constant rate, and (2) a double exponential emission profile for each instantaneously applied segment.

Representation of the time

- varying source strength for a double exponential during a continuous painting event is given by the following eq uation: 1



## ( ) 

# ( ) ( ) 

# ( ) 

# [ ]

( ) 

# ( ) ( ) ( ) 

# ( ) 

# [ ] ( ) S t M

t f e f e

M

t f e f e H t t

a k t k t

a k t t k t t

a a a = − +

− −

− − +

− − − − −

− − − − 1 1 1

1 1 1 1 2

1 2 (A1

- 3)

where: M = Total mass to be emitted (mass)
t a = Paint application time (time) f = Fraction of mass emitted from the first exponential (unitless)
t = Time

and: 

## ( ) ( ) ( ) H t t if t t if t t a a

a − = − ≤

− >    0 0

1 0

;

; (A1

- 4)

Equation A1

- 3 provides a continuous representation of the emission rate during and following application, where the source emission for each instantaneously applied segment is represented by a double exponential. This equation has bee n used in the past for representation of emissions and concentration profiles during painting by EPA studies. 2

, 3

1 WC Evans, “Development of Continuous

- Application Source Terms and Two

- Compartment Analytical Solutions,” ASTM Symposium on Method s for Characterizing Indoor Sources and Sinks, September 25

- 28, 1994, Washington, DC

2 Wilkes C, Koontz M, Ryan M, and Cinalli C, Estimation of Emission Profiles for Interior Latex Paint, Proceedings: The Seventh International Conference on Indoor Air Qual ity and Climate. Nagoya, Japan, July 21

- 26, 1996, Vol. 2: 55

- 60
3 Wilkes, CR and MD Koontz. Use of Small

- Chamber Data to Estimate and Model Chemical Emissions from Latex and Alkyd Paints. Final Report No. IE

- 2812, prepared by GEOMET Technologies, Inc. un der EPA Contract No. 68

- D3

- 0013, WA No. 3

- 19, 1997.

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### Attachment 2: Application of the Model Algorithms

The purpose of the model algorithm is to provide a representation of the emissions and r esulting air concentrations affecting exposure to the product user and other occupants of the home, with the objective of performing model

- based exposure assessments to evaluate a variety of exposure scenarios. Ideally, to accomplish this the model algorit hms need to be scalable across factors impacting the exposure, including housing characteristics, product use alternatives, and other paint stripping activities.

The basis for the model algorithm was limited to selected chamber studies published in the literature as cited in the NMP and DCM Risk Assessment documents. Since no theoretical

- based model developed and validated was found, the approach pursued for this project was to develop semi

- empirical algorithms to allow scaling across the important parame ters of interest. Based on the similarity of paint stripping activities and past studies investigating exposure to paint products, 2 , 3 the double exponential was eva luated. Furthermore, the incremental application of the double exponential was considered to represent the continuous nature of the product use. Selected advantages, disadvantages and shortcomings are listed in Table A2

- 1.

Table A2

- 1. Selected Advantages , Disadvantages and Shortcomings of Each Considered Model Algorithm
Algorithm Advantages Disadvantages/Shortcomings A

Single Exponential Captures the general emission characteristics

Scalable to product mass Cannot capture more than one process

. Not

sc alable or translatable to other variables.

Double Exponential Considers fast and slow emissions

Scalable to product mass Not scalable or translatable to other variables.

Cannot represent the continuous nature of the product application Incremental Dou ble exponential Scalable (Partly or Fully) Product amount Product Use Rate Duration of application Room size Ventilation rates Some variability not explained

Not necessarily translatable to other application methods, very different formulations.

A. No t all shortcomings are mentioned

. For example, there is not mass

- transfer model that accounts for resistance to evaporation that may occur as the air concentration approaches saturation.

A comparison of the fit to the NMP data from the MRI study is prese nted in Figure A2

- 1. The NMP data shown in Figure A2

- 1 are taken from NMP chamber study results conducted by Midwest Research Institute under contract to the US EPA. 4 The data shown in Figure A2

- 1 were collected during three chamber tests, referred to as R uns 10, 11, and 12, during which a brush

- on paint stripper product containing NMP was applied to a 2 x 4 foot painted plywood panel in a controlled environment, and the resulting air concentration were monitored. The paint stripper

4 EPA, 1994. Consumer Exposure to Paint Stripper Solvents. Final Report. U.S. Environmental Protection Agency. February 28, 1994.NMP Review

– Response to Comments (Continued)

application and scraping activity lasted 107, 112 and 113 minutes for Runs 10, 11 and 12, respectively. Approximately 5 hours of airborne concentration data were collected per run.

Two alternative double exponential representations are shown in the figure as well as an increment al adaptation of the double exponential (see discussion in Attachment 1). Two of the three fits shown in the figure were derived based on minimizing the sum of the squared differences between the model predictions and the observed data, allowing the fittin g algorithm to vary the rate constants and the mass of chemical applied to each algorithm. The incremental model shown in Figure A2

- 1 was fit to the Run 10 data using 8 double exponentials as illustrated in Figure A1

- 1, each representing approximately 14 m inutes of painting/scraping activity. The fit to chamber test 10 was performed because the data from that test was judged to be best behaved data set.

As Figure A2

- 1 demonstrates, the double exponential does not fit data as well as the incremental version of the double exponential model, for both the initial rise and the tail portions of the concentration profile. Likewise, the parameters suggested by Armstrong do not result in a fit that captures both the shape and the magnitude of the concentration profi le.

Figure A2

- 1. Comparison of the Four Considered Models for Representing the Chamber Data

.

12