Tom McKone tests alternative way to assess environmental risk

Is 50 degree weather cold or mild? Is 45 years of age old or young? If you scored 85 on a test, did you do well, O.K., or poorly? If you agreed to meet at 11:00 a.m. and arrived at 11:15 a.m., was that acceptable or rude?
Your frame of reference can make a big difference, says COEH faculty member Tom McKone of UC Berkeley. Language can be imprecise, and perceptions vary. So when researchers and government officials attempt to assess environmental risks and set policy, they opened themselves dealing with uncertainty and ambiguity about what constitutes a situation that is "hazardous," "acceptable," or "safe."
Seeking a way to characterize more clearly the uncertainties involved in assessing environmental risks, McKone and colleague Ashok Deshpande, now retired from India's National Environmental Engineering Research Institute, have borrowed a methodology used in engineering that takes into account the extent to which something .ts into sets of de.ned categories.
For example, McKone says, many people would call a Fuji apple red. But it has yellow in it as well. How can we account for the fact that the apple can be categorized as red and, to some extent, not red (yellow)? Fuzzy logic, the name given the methodology by its inventor, Lot. Zadeh of UC Berkeley, provides a way to express the apple's "degree of membership" in both color categories.
Deshpande and his colleagues in India applied fuzzy logic to an assessment of water quality in the Ganges River. McKone worked with Deshpande to interpret the general applicability of this work to risk assessment. Tens of thousands of people bathe in the Ganges each day for religious reasons, yet the quality of the water in the river has deteriorated over the years as sewage and industrial wastes have been discharged into it. To help the government assess the effectiveness of measures it had introduced to control the effluent, the researchers used fuzzy logic methodology to study the water at two bathing places, Varanasi, where pollution has been severe, and Rishikesh. By considering both water quality measurements and expert opinions about what measurements are acceptable, they were able to characterize the water quality at Rishikesh as "acceptable" and to demonstrate an urgent need to intensify pollution control efforts at Varnasi, where the water quality was "not acceptable."
"But Are We Safe?"
"A lot of the things we do in public health or environmental decision-making really involve these fuzzy sets," McKone says. "We don't know what 'safe' is. You can't draw a straight line and say everything on this side is safe and everything on that side is dangerous. We like to have crisp lines, but that gives us a false sense of safety--'Oh, drink all you want, because your water is at .8 parts per million and the standard is 1.'"
The traditional numerical approach to risk assessment is hard for the public to understand, McKone says: "I've been to so many meetings where somebody gives a long presentation and concludes, '…and your risk is 10-3,' and everyone is scratching his head and asking, 'But are we safe?' I think it would be a very interesting experiment in risk communication if we came into a community and said, 'We looked at what makes water unsafe, what makes water acceptable, what makes water good and what makes water very good. Your water has a high degree of match with good but not with very good.' So people can think about what they want to do. People don't understand the numbers-all they hear is the word, risk. If you can keep the assessment process in the domain of lay-language and away from mathematical terminology, it may be easier for people to digest."
McKone says regulation is moving in that direction: "Many regulatory agencies are now putting a lot more narrative discussion into the risk assessment and are talking about margins of safety rather than risk. Our methodology provides yet another alternative. It allows us to articulate a range of uncertainties by putting them in categories."
In public health, McKone says, the way you communicate information can influence the decisions you get. He doesn't think fuzzy logic leads to radically different scientific outcomes, but he believes it can help scientists and regulators explain complex environmental challenges in a way that makes the uncertainties and the trade-offs more transparent.