Sunday, January 26, 2014

IH’s Dirty Little Secret

Blogging is a remarkable media; indeed, this blog has opened communication among colleagues in a manner that I did not anticipate.  This week’s blog represents a prime example.

You may remember that two of the last three blogs deal with the subject of sampling statistics in which I tried to make the point that exposure is naturally quite variable and that by just taking one or a few samples, we can be tricked as to whether a workplace is really in compliance relative to an OEL.  The blogs advocated using good statistical software such as IH STAT to help interpret the data. 

In response to these blogs I heard from a respected colleague who has a lot more field experience as an IH than I do.   Indeed, I would describe him as a seasoned and very thoughtful exposure assessor.   Because he does not want to jeopardize his future employment opportunities he has asked that I keep his comments anonymous which I am pasting below: 

“I think that the AIHA as an association has failed to a great degree to educate industry about this particular failing of our profession [the severe limitations of small sample size].  I'm not aware of a single piece that has ever been produced that attempts to educate the layperson (most business owners are) about the limitations of small sample sets.  

I also think that a pertinent question has been missed by the IH community.  That is - a reality-based question:  would a chemical-handling business be better off by having a professional IH visit and observe their processes, and conduct a single sample vs. no visit at all?  I have lost sleep over this.  Having worked with hundreds of small (and medium, large) businesses over the years, I recognized this issue soon after attending my first continuing education course on statistics back in the early '90s.

The reality is that a sizable proportion of chemical handling businesses will NEVER find their way to taking ANY samples.   Of the ones that do, VERY FEW would ever entertain the idea of repeat sampling.  This bothered me so much, that for a while I started giving proposals to clients with an explanation of my own regarding statistical limitations, and providing them two options in proposals - one with a single visit/sampling event, a second (obviously more expensive) option that would provide more confidence in conclusions reached - and leave the decision with them. Maybe I'm not a very concise scientific communicator, or just not a good salesman, but NONE of them ever opted for the multiple sampling option.

Another issue addresses a level playing field for consultants. This is really important because if you look at the demographics of the AIHA, consultants are the single largest group, and likely perform more than half of all exposure assessments. If I were to immediately insist that my new customers only go the repetitive sampling route, I would be out of business as a consultant tomorrow.  There will always be someone else to step in that gives the customer what they want. Luckily, my larger, more sophisticated clients will opt for building data validation over time through repeated sampling events, but they are still few and far between.

Lastly, and sadly – OSHA regulations simply don’t address the issue adequately.  Other than a few of the substance specific regulations for repeat sampling, there is no legal impetus for employers to provide a statistically sound approach to exposure assessment.

Hey - thanks for letting me spout off!  This is truly the 'dirty little secret' of the IH field, and in my opinion - no amount of shaming is going to solve this.” 

I appreciate this expression of opinion and truth and I know this person enough to believe he is correct.

From my perspective, I believe that taking one sample is MUCH better than taking none as long as you attempt to factor in the very large uncertainty associated with such action.   Assuming that a geometric standard deviation of about 2 is typical for workplace exposures, then, in my opinion, a single sample value that comes out to be less than 10% of an OEL is a pretty good indication that the average exposure will be below the OEL at least 90-95% of the time.  If true, OEL/10 could become the new action level for single samples.   Indeed, I would love to have some sharp statistical minds to look over this suggested approach and comment.

Another way of appropriately using but a single sample is to be sure you monitor reasonable worst case.  That is, if possible, be sure that your single sample is capturing reasonably foreseeable worst case.  Such factors as maximum product rate and/or minimum ventilation rate would be examples of this approach. 

My sense of all of this is that it seems to point directly to the value of modeling in determining actually what scenarios and conditions should be monitored using our precious resources. 

Sunday, January 19, 2014

Modeling and the Industrial Hygienist in the Real World

I have gotten a number of comments recently suggesting that I advocate modeling as an isolated intellectual pursuit, typically characterized as an office and desk-bound activity in favor of getting one’s hands dirty as an IH in the field.   Indeed, one reader recently wrote:

“Getting one's hands dirty in a workplace is only for pump jockeys, right?
 "Your blogs are starting to sound like only schmucks actually take air samples."

My first reaction was shock and dismay by the comments; however, on further reflection they only shows that I have not been clear relative to some important aspects of what I am trying to convey with these blogs. 

Of course, everything we do as IH practitioners should be aimed at understanding the reality that drives worker exposure.  That definitely means getting one's hands dirty while being there in their world and living and experiencing that environment. 

It is clear to me that any IH who does this without invoking any formal modeling framework still does so within an informal modeling framework.   From their intimate experience in the workplace (i.e., getting their hands dirty) I believe that they run subliminal models that unconsciously crunch the exposure numbers in their brain to conclude:

·         This is a safe environment that does not need to be monitored
·         This may not be a safe environment and needs to be evaluated.
·         This is definitely an unsafe environment and needs to be managed.

This is sometimes called Qualitative Risk Assessment born of Expert Judgment.    It is a time-honored approach that has worked for many years to keep workers safe.   The IH folks with more experience or with somehow better brains get better and better at this and become experts.

All that my colleagues within the Exposure Assessment Strategies Committee ( and I am are trying to do in all this is to introduce a more formalized and scientific approach of using models to provide a blueprint for what may be happening in any workplace.

Using this more conscious and formalized approach helps us to see what is driving the exposure and to quantitatively predict (within uncertainty bounds) what the exposure potentials might be.    It organizes all the information into a coherent form that can be tested,  modified and improved using the basic scientific method.   It enables us to become experts much sooner and it allows us to teach the coming generation more quickly to be expert.   Finally, it provides accountability and transparency to our actions so that our clients can see the basis for our decisions and recommendations.  

The necessary part about sitting at a desk and using a computer is not the essence of what is being proposed as the modeling approach.    The models are simply constructs of the workplace reality and they need to be fed with good inputs that reflect that reality and that means truly understanding and experiencing the workplace.   That includes getting our hands dirty.   We can best learn about the parameters of contaminant sources, transport and contact with those sources by being there in the work place and also by talking with the workers. 

This blog is not meant to denigrate traditional IH expertise and efforts.  Our intention is just the opposite, the traditional drive inherent in this activity and the resulting expertise offers the perfect example of professional focus.  Our efforts are simply designed to provide folks with the technical tools and opportunity to substantially enhance their power as professionals.

Sunday, January 12, 2014

IH DIG and the Pump Monkey

The last blog was the confession of a former pump junky; namely, me.  Pump jockey is a somewhat disparaging term but I have heard worst; namely, the very insulting designation "pump monkey".  This week we get to see just how “monkey like” we really can be when we rely just on just our judgment to gauge exposure data.   This opportunity is presented in the form of a free smartphone app:  IH DIG.  

IH DIG is a very cleaver game that presents the player with airborne exposure monitoring data and asks the player to make a judgment about the meaning of that data.   Talk about a situation that is right up our alley!

IH DIG presents the player with 20 sets of air monitoring data one set at a time.   Each set has between 1 and 8 air monitored values and an occupational exposure limit (OEL) for comparison.  It asks you to look at the data and determine whether it indicates an exposure (95%tile) in one of the 4 standard AIHA exposure categories:   

1.      Less than 10% of the OEL
2.       10-50% of the OEL
3.       50-100% of the OEL
4.       Greater than 100% of the OEL
Remember in the last blog we discussed that there is a considerable amount of day-to-day variation in exposure even in the same job done by the same person and that we needed a statistical benchmark that used this variation to determine whether the exposure exceeds the OEL.    Here we (and IH DIG) are using the 95%tile upper bound limit on the exposure to estimate which category the exposure data best fit. 

Just to give you an example, IH DIG presented the following sampling set: 307, 152, 23 mg/m3 with an OEL of 500 mg/m3.  I guessed category 3: (50-100% of the OEL) and was immediately told I was wrong and that it is actually in category 4:  >100 % of the OEL.    IH DIG does this for 19 other data sets and then scores your overall ability to judge exposure when you go forward without the benefit of a sophisticated statistical tool like IH STAT.  If you do very well you are declared to be a "Super IH"; however, if you perform very poorly you could be awarded the unappealling title of "Dart Throwing Monkey"!  

If you play IH DIG enough you will get better at judging which category is correct.   Indeed, I believe it actually makes us more aware of the reality of uncertainty within our data; however, I have never gotten a perfect score and there always seems to be some surprises.    The lesson is clear; we need the benefit of some good statistical analysis software like IH STAT.

I think IH DIG would make a great training tool for new Industrial Hygienists.   It really does present an object lesson and can be very educational if not humbling.

IH DIG is available for I-phone/I-Pad and Android smartphones and tablets.    I think the best way to get it is to fire up the browser/search engine on your smartphone or tablet and put in “IH DIG app”.   The first hit should be the AIHA Exposure Assessment Strategies Committee page with a link half way down the page to the IH DIG app download in either flavor.



Monday, January 6, 2014

Confessions of a Pump Jockey

I admit it.  Early in my career I was a Pump Jockey.   I have received my basic training in air monitoring and I was quite proud of the fact that I could calibrate a pump and work out all the logistics of air monitoring.  Indeed, it was somewhat magical and heady for me to realize that we can sample the air and actually determine the concentration of specific chemical species within the air of the breathing zone of workers.

Armed with my list of Exposure Limits (both ACGIH TLVs and my company’s internal limits) I was ready to take on the world of Industrial Hygiene.   I was hot stuff!     I understood the basic premise that a ratio of Exposure/Exposure Limit less than one had a happy face J while an exposure above the exposure limit required some action L.   Confidence was high and self-doubt and introspection relatively low.   If an exposure limit was 10ppm and I measured a breathing zone exposure of 20ppm I was pretty sure that an overexposure had occurred L.   If I took a single measurement of a breathing zone exposure of 2.1ppm for the same compound I would tend to declare the situation “safe” J and not consider doing any more testing.   If I was the least bit unsure and took another sample (same scenario, same worker, and different day) and got 4.2ppm I would still tend to think that this average exposure that was less than 50% of the OEL was safe.    If for some crazy reason I took a third sample and got 8.4ppm my confidence might be shaken somewhat but I could still rationalize that the mean and median measured exposures were still below 50% of the OEL often considered to be the “action level” or point where you would do something to control the exposure L .

Enter statistical analysis and my introduction to reality.  Indeed, I eventually I learned that exposures in essentially all workplace environments are quite variable even for the same worker doing the same job.   I learned that most exposures are well described by either a normal or lognormal distribution.   The normal distribution is the “bell shaped curve” that has probabilities for every exposure value with likelihoods for those values.   The area from the top of the bell to the left (toward negative infinity) has 50% of the exposure values and the area to the right toward positive infinitely has the other 50%.    So if the population of exposure numbers is highly scattered or diverse then the width or spread of the bell is relatively broad.    It should be noted that the numbers never end, they go to negative infinity to the left and positive infinity to the right.  So there is always some finite (but often vanishingly small) chance of any exposure in this distribution.   A lognormal distribution is just the distribution of the log of all these exposures.   This distribution of exposures in a lognormal distribution is bounded on the left by zero (just like the real world) and positive infinity to the right.  It is skewed or pushed over to the left which means it is asymmetrical with more values of exposure concentrated toward zero (just like the real world).    Indeed, in general, the lognormal distribution does a much better job of describing the distribution of real world exposures in any homogeneous scenario and should be used by default as long as the data passes a fit test of the lognormal assumption.

The above is statistical reality but what we folks in the field need is a user-friendly statistical tool to put this rubber to the road.   There have been a number of candidates over the years but the latest and, in my opinion, the greatest is IH STAT developed by Dr. John Mulhausen who is the Director of Corporate Safety and Industrial Hygiene at 3M Company.   John developed the original spreadsheet program over the years where it has been modified into its current multilingual version by Daniel Drolet.   You can get it at:   For us English speakers, I suggest downloading the “macro free version” for ease of use.

As an exercise let’s put our data 2.1, 4.2 and 8.4 ppm into IH STAT and see what we get.    The program advises that the data fit both the normal and lognormal distribution but fit the lognormal better.   The error bands around the estimates of the mean are very broad primarily because we only have three samples.  Statistically, the model is much “happier” with 6 or more samples but that was frankly unheard of in my pump jockey days.

The statistical lognormal fitted model has a geometric standard deviation (GSD) of 2.0.     This represents the width of the lognormal curve as discussed above and a value of 2 is pretty typical.   Indeed, it is not until the GSD gets to be greater than 3 that the process is considered to be out of control or the exposure group poorly defined. 

What is most interesting about this analysis is that the lognormal distribution predicts that greater than 10% of the time the OEL will be exceeded in this exposure scenario.   That would mean that for more than 25 days in a 250-day working year the exposure in this scenario would be predicted to exceed the exposure limit (OEL).   If I had known this in my heady days as a pump jockey it would have given me pause.  Indeed, there was advice around even on those days from NIOSH that if the GSD was 2 then the “action level” should be about 10% of the OEL.   Thus, the above data were all above this recommended action level.   Unfortunately, absent wonderful tools like IH STAT, few were doing detailed statistical analysis in those days (the 1970s) and I certainly was not.

The Pennsylvania Dutch have a wonderful saying:  “Too soon old and too late smart”.   It is definitely not too late for you rise from pump jockey status to that of exposure assessor using this remarkable tool.