Exposure Modeling Leading to Discovery

Models can often present us with remarkable learning experiences.   Indeed, combining model development with the necessary aspects of experimentation to feed a model can lead to some important discoveries.  This week I am going to recount a discovery that my colleagues and I had while developing a model of the indoor air exposure potential from the off-gassing of pesticide from treated wood.

The pesticide was an important product for the company I was working for at the time and as the Manager of Human Health Risk Assessment, it was my responsibility to conduct a risk assessment on the use of the product as a wood preservative used on wood in the indoor environment.   

Our first experiment was to put the treated wood into a glass chamber and measure its concentration in the chamber’s air.  From this and other experiments, the generation rate of the off-gassing was estimated. 

An early lesson in all this is that models and some simulations are not reality but simply a portrayal of reality.   If they are reasonably portrayals, we can learn something.  Indeed, glass chambers are not real rooms but these results provided a lot of information about the ultimate use of treated wood used indoors in residences.

The system was complicated enough that we decided to do dynamic modeling of the chemical in compartments in a manner that is completely analogous to physiologically based pharmacokinetic (PBPK) models (https://en.wikipedia.org/wiki/Physiologically_based_pharmacokinetic_modelling). That is, each compartment is conceptually constructed to describe the instantaneous and integrated rate of pesticide input to and output from it.   While in any compartment, there was an option to describe changes or reactions of the pesticide during its time in that compartment.

The compartments we chose in our glass chamber experiment were:

·         The treated wood

·         The air space in the chamber

·         The chamber walls

A remarkable bit of technology that allowed us to do this was Advance Continuous Simulation Language (ACSL) software  (https://en.wikipedia.org/wiki/AEgis_Technologies).   At the time the best way to run this software was on mini-computers (known colloquially as “pizza boxes” because of their shape and size) running UNIX with a program subscription from  the Dow Chemical Company called SimuSolv.    I mention this because there was a lot of expense (10s of thousands of dollars) involved in getting a “pizza box”, having the technical IT support to run UNIX on this minicomputer and the cost of the license for SimuSolv.    Today you can get a PC license for ACSL for as little as $500  (http://www.acslx.com/sales/).   I am not trying to sell or advertise ACSL.   My intent is only to point out that ACSL is a good modeling tool that we went to a lot of trouble and expense to use it but it has gotten a lot more affordable over the last 20 years.

We ran a number of experiments to feed this model and during our initial analysis could not make the model work!

In our initial model, we assumed that there was NO reaction of the pesticide while it was in any compartment.   As a result the model predicted exposures that were about 5 times higher than what was actually measured in the glass chamber.   Clearly, the model got it wrong and needed to be refined to account for the "lost" material.

We asked the synthesis chemists about the stability of the pesticide in air or on glass surfaces and they said that it should be very stable for the time frame we were measuring (a few hundred hours).  

 

Because our model did not work, and notwithstanding the Chemists' comments, we hypothesized that perhaps that the combination of long residence time on the internal chamber glass surface combined with the large surface area-to-volume residue of the pesticide film on the glass could indeed lead to degradation.  This degradation would come from reaction with oxygen or trace amounts of tropospheric ozone or other reactive species present in the untreated suburban air used to ventilate the chamber. 

We changed the model to allow for degradation while on the chamber walls and SimuSolv allowed us to optimize the model for the degradation rate that provided the best fit to the data.    This eventually led to 0.005/hr as the estimated rate of degradation.  In 100 hours this predicts that 50% of the deposited pesticide would have degraded to other, typically less toxic species.

The Chemists congratulated us on our model fit but said that they did not believe that degradation was occurring.    That led to another series of experiments where we demonstrated after putting essentially pure pesticide on glass it was significantly transformed to almost a dozen chromatographically distinct species of compounds after prolonged exposure to ambient air.   Clearly, the significant rate of degradation that the model predicted was occurring.

The initial failure of the model allowed us to discover this important mechanism that was driving the concentration in the glass chamber.

Clearly, most of us do not live in glass houses and subsequent experiments with real rooms showed much stronger effects presumably from absorption (probably with degradation) were in play; however, the lesson here should not be lost.   Modeling can lead to some important discoveries.

We published most of the above work in the AIHA J and I would be happy to send a copy to anyone who requests it from me at:  mjayjock@gmail.com

Exposure Modeling Research - The Time is Now

For someone who has been advocating the modeling of exposure estimation for many years, it is very heartening to see research in this area taking root and growing.

Twenty-four years ago this spring, a friend and colleague, Neil Hawkins, suggested that I meet with a young woman who was an IH working for Dow Corning.  Her name was Susan Arnold and Neil said that she was very bright with a lot of energy and that I should talk with her about exposure modeling.   I contacted Susan and we went out to dinner at the AIHA Annual Conference in Salt Lake City in the spring of 1991.    We have been friends and colleagues ever since and Susan has worked as a modeler ever since.   Indeed, she received her Master’s Degree with a modeling project and will defend her PhD thesis on modeling at the University of Minnesota later this summer.  Suffice it to say that Neil and I are very proud of Susan and her accomplishments.  

At this month’s conference of the American Industrial Hygiene Association in Salt Lake City, I and many of my colleagues were treated to some of the excellent work coming out of the University of Minnesota under the leadership of Dr. Gurumurthy Ramachandran or, as many of us know him, Ram.  

On the 24^th anniversary of our first meeting in Salt Lake City, Susan presented three papers on modeling which I will mention very briefly here and send her slides to whomever asks for them.

For many years Susan, Ram, Perry Logan, John Mulhausen and others have been interested in investigating the nature, power and accuracy of “expert judgement” within the realm of industrial hygiene.    Indeed, since the beginning of the profession the mantle or cloak of “expert judgment” has been invoked most times an IH would declare  a particular exposure scenario to be “safe” or in need or further investigation.   The term was so ubiquitous that it begged to be defined.  This was done in the latest (and I believe earlier editions of) AIHA Exposure “Strategies Book”.  The quote below is from the 3^rd Edition:

“The application and appropriate use of knowledge gained from the formal education, experience, experimentation, inference, and analogy.  The capacity of an experience professional to draw correct inferences from incomplete quantitative data, frequently on the basis of observations, analogy and intuition.”   

The nature of professional judgment of Industrial Hygienists has been put to the test by asking them to use their judgment to characterize well-described exposure scenarios (without monitoring data) by placing them in one of 4 bins; namely, less than 10% of the OEL,  10-50% of the OEL, 50 – less than 100% of the OEL and greater than or equal to the OEL.  When asked to do this without information provided by modeling they systematically underestimated the true exposure.

Note: Even when you have monitoring data, characterizing or placing the exposure  in the correct bin is challenging.  If you do not believe me, read a previous blog on the Smart Phone App:  IH DIG (http://jayjock-associates.blogspot.com/2014/01/ih-dig-and-pump-monkey.html).  Play IH DIG and you will understand. 

Susan’s three presentations get into the issue of professional judgment aided by modeling while putting some of the most popular models through their paces in both the laboratory and real world.   The titles of the three talks she presented are:

• Evaluating Model Performance under Highly Controlled Conditions
• Evaluating Model Performance under Real World Conditions
• Predicting Construction Related Silica Exposure Using Input from Chamber and Field Studies

 As mentioned above, send me an email request (mjayjock@gmail.com) and I will send you these slides.

Research into exposure assessment modeling is really just getting started; there is still plenty of room for folks to get involved in this growing field.  Indeed, as Susan wrote in the last conclusion of one of her talks:  “A very young science… there is still much to learn!

New Research into Eddy Diffusivity (D)

One cannot teach (or blog) without learning.  It is one of the very real perks of trying to convey knowledge and information. 

At the recent conference of the American Industrial Hygiene Association in Salt Lake City, I and many of my colleagues were treated to some of the excellent work coming out of the University of Minnesota under the leadership of Dr. Gurumurthy Ramachandran or, as many of us know him, Ram.   Two of his graduate students presented their work which I will be summarizing here over the next few weeks. 

This week, it is my pleasure to summarize the presentation and work of Yuan Shao who told us of his efforts to determine of Eddy Diffusivity Coefficient (D) from more easily measured quantities such as ventilation rates and room dimensions.  

You may remember a blog I did some time ago on this subject published on December 30, 2013 (http://jayjock-associates.blogspot.com/2013/12/the-eddy-diffusion-near-field-model-is.html) entitled:  The Eddy Diffusion Near Field Model is Now Useable.   In that 2013 blog I discussed how the Eddy Diffusivity Model should be ideally suited for modeling many indoor sources; however, the major problem with the use of the model is the determination and use of a critical model parameter; specifically, the Eddy Diffusivity Coefficient (D).  Indeed, the predictions of this model are highly depended on D as defined below.

The critical variable D is dependent on how the air moves about randomly within the indoor environment.  Unfortunately, it (D) has historically proven itself to be very difficult to measure or estimate.   As a result many of us wishing to use this model have been forced to use a very wide range of estimates for D.  As such the utility of this model has been quite limited.   In that blog I discussed the research of Dr. Kai-Chung Cheng from Stanford University and his work to relate D to the ventilation rate expressed as air changes per hour and the room’s dimensions.   I noted Dr. Kai-Chungs work as a real advancement in our ability to use the Eddy Diffusivity Model which, by the way, is one of the available modules in the freeware spreadsheet: IH MOD.

It would appear that Yuan Shao has advanced that effort and provided us with more data and analysis of this important topic.   His conclusions are presented below: 

•    An exposure chamber was constructed to create conditions for the eddy diffusion studies.
•     A diffusion model accounting for chamber boundary, advection and the removal of contaminant due to the local ventilation system was developed.
•     In this study, the measured and modeled data fit well over a range of experimental conditions. There is a strong linear relationship between D and ACH, providing a surrogate parameter for estimating D in real-life settings.
•     The values of D obtained from the experiments are generally consistent with values reported in the literature.
•     These findings make the use of turbulent eddy diffusion models for exposure assessment in workplace environments more feasible.

This is exactly the type of work that has been needed for many years but is now coming out as a result of these excellent research programs.

Yuan Shao has given me permission to send his full slide deck to whoever asks me for it at:  mjayjock@gmail.com.

As always, I would be very interested in your comments about this work and your experience with the Eddy Diffusivity Model and IH MOD.

Having a Hammer as a Sole Tool Focuses Your View of Problems to Nails

A noted psychologist, Abraham Maslow, is credited by some as coming up with one of my favorite quotes which I am paraphrasing below:

“If the only tool you have is a hammer, 

  you will see every problem as a nail”

Our Industrial Hygiene tool kit is rich in tools designed to assess the exposure and risk from the inhalation of toxicants.   Indeed, essentially all of our exposure limits (TLV, PELs, OELs, etc.) are set as airborne concentrations that might occur in the breathing zone of workers.   I am unaware of any similar compendiums of dermal exposure limits but my readers have pleasantly surprised me in the past.  So if you know of any please send me an email.   mjayjock@gmail.co.

Indeed, if a chemical has a relatively high molecular weight (say >200 Daltons) and an octanol water partitioning coefficient of greater than 100,  its exposure potential will most like result more from dermal exposure than from inhalation.  Indeed, I seem to remember biological and air monitoring studies done with pentachlorophenol in open wood treatment lines showed that the majority (>90%)  of the systemic exposure/dose to the workers came from dermal rather than inhalation exposure.

I met Chris Packham in London many years ago and he struck with his focus and dedication to the science of control of worker health risk from dermal exposure.   Clearly he has continued that dedication with his more current teachings and writings.   The following quote was taken from a document that he recently sent and me and does indeed provide food for thought:

It is well established that inhalation of toxic chemicals can result in systemic effects, i.e. damage to internal organs and systems. A great deal of research and development has been undertaken resulting in strategies and equipment to monitor inhalation exposure. As a result in many countries there are exposure limits for a wide range of chemicals. Far less attention has been paid to the potential for chemicals to penetrate the skin and either cause or contribute to systemic toxic effects. Yet there is considerable evidence showing the potential for skin exposure to do this, including with chemicals that are unlikely ever to be inhaled because of their physical properties.(1) There is also a view that inhalation exposure results in more serious damage to health than can occur from skin exposure, often regarded as “just a rash”. Yet the EU Classification, Labelling and Packaging Regulation (EU1272/2008) contains the Hazard Statement 'H310 – Fatal in contact with skin'.

In this article the author will review the evidence showing why, in considering risks of damage to health due to the use of chemicals, the potential for skin exposure to cause systemic damage must be an integral part of any chemical exposure risk assessment.

If you would like the full text of this piece by Chris, just let me know at mjayjock@gmail.com and I will send it to you.

Chris, has also had a recent (February 2015) piece printed by the British Occupational Hygiene Society on this subject that I would be happy to send to you as well.

I would be very interested to hear how readers of this blog address dermal exposure and risk assessment and how these efforts compare to what is done for inhalation risks.