Chronic Chemical Exposure Requires a Rethink

Industry must pay more attention to the impact of long-term contact with chemicals

By Dirk Willard, contributing editor

“These environmental regulations are stupid,” bellowed the operator. He often demonstrated his contempt for them by dipping his arms in toluene, carbon tetrachloride and other solvents. Engineers at Upjohn constantly warned him to wear his personal protective gear but he took no heed. Eventually, he retired. Shortly afterwards, when visiting his friends at work he often complained of forgetfulness, dizzy spells, nervous tremors and other maladies, which he chalked up to old age. A few years later, he died of cancer, his body and brain riddled with tumors.

Even lawyers understand and accept acute exposure limits.

This is a perfect manifestation of a chronic illness, i.e., one caused by long-term exposure to a toxin. There are many anecdotal examples, e.g., people such as frequent flyers continually exposed to low-dose radiation developing skin cancer. Not all information is anecdotal, though. In the 1930s, Sweden documented the shortened life expectancy of organic chemists — they died of cancer. The State of California reported a 12–25 year latency period between exposure and the development of tumors.

Companies weren’t always concerned with chemical exposure. One worker in 19th century England explained to a doctor how men died when exposed to chlorine gas: “It’s like this. You get gas. We run to the office for the brandy bottle and say, ‘So-and-so’s got gas.’ Brandy is served out. You go home and die. Doctors say you died of faint, and the proof is that brandy was needed to revive you.” (p. 132, Peter Macinnis, “Poisons: from Hemlock to Botox and the Killer Bean of Calabar,” Arcade Publishing, 2011.) Today, laws require industry to take an active part in preventing chemical exposure. Unfortunately, the type of exposure can make compliance difficult.

Six Steps To Safer Processing

The effects of acute exposure are relatively easy to understand. The U.S. Environmental Protection Agency (EPA) and the National Institutes of Health, particularly the National Institute of Environmental Health Sciences, have done a superb job of connecting laboratory animal exposures to human exposure limits. Even lawyers understand and accept these limits. Of course, the impact of exceeding an exposure limit depends somewhat on chance, environmental factors and individual constitutions; some people develop an allergic reaction that worsens toxic effects, while others develop immunity. Rasputin survived a dose of potassium cyanide that would have killed several men.

Chronic damage not only is more insidious but also is far harder to conclusively pin down to a particular long-term irritant. Statistics for the impact of chronic exposure never are definitive. This lack of certainty has afforded a good living to many lawyers.

The EPA addressed second-hand smoke without data with a 99% confidence level that showed ill effects. It hinged its argument on the existence of “prior evidence” of harm. That same reasoning should apply to chemicals. If a substance causes acute damage, such as tremors, then a chronic illness, such as growth of brain tumors, should not require data with a 99%-confidence level to prove the link.

Because low-level exposure can result in a chronic effect years later, perhaps we in industry should reduce our exposure limits to those specified for the most sensitive members of society: pregnant women and children.

We also should consider monitoring the chemical exposure of operations personnel by requiring annual or semi-annual physicals. When I was conducting research in the Air Force, we endured annual physicals that estimated our exposure to various heavy metals and other chemicals used in propellant development.

dirk.jpgDIRK WILLARD is a Chemical Processing contributing editor. He recently won recognition for his Field Notes column from the ASBPE. Chemical Processing is proud to have him on board. You can e-mail him at

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  • What the EPA hoped to get at with its argument against statistical analysis is the strict adherence to statistical theory where risk of harm has already been proved. Part of this has to do with a bias against single-tail, Type I error; the tails are either end of a bell curve. Here my explanation wanders into the mechanics off statistical theory. In binary terms of yes or no, a theory (null statement) being tested is either right or wrong. Therefore, we have two types of error: Type I and Type II. Put simply, a Type I error is deciding your theory is wrong when it’s correct. Type II error is the opposite: deciding your test theory is right when it’s wrong – statistical testing is purposely biased against this outcome. If your test was whether Joe had Ebola I think you’d want to rule out that he had it, not blindly rule that he did not. Here, a Type I error, typically based on a 99% confidence, could get you (us) in trouble. The other problem with traditional statistical analysis is the rigors of the confidence interval. Using a lower confidence, say to 80% , or 90% as the EPA did for second-hand smoke, might cut into the Type I error bias. For more information refer to:


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