Risk assessors endeavor to define a risk that will be realized under actual or anticipated conditions by establishing the "average truth" from numerous probabilities. Assessing risk based on conclusions drawn from an infinite number of integrations cannot be expressed in terms of "safe" or "non-toxic." Such expressions imply that a chemical or an event (e.g., accident) is without risk or harm, which may be misleading. Instead a chemical or an event is ranked as minor, moderate, or high to reflect the degree of risk that it represents within a given set of parameters. Such is the role of the risk assessor to calculate the effects of variables while classifying the uncertainties and presenting risk managers with a list of choices.
Comprehending the number of variables and uncertainties associated with an event can be explored in the following example. If one were to assess the number of deaths from cancer following a lifetime exposure to a chemical contained in drinking water, one must examine a diverse array of interlocking factors, such as the composition of the chemical, the chemical's observable effects on animals and/or environment , and whether the reported dose-exposure rates are applicable to humans. Likewise, if one were to examine the amount of chemical which can remain in the soil and not create groundwater contamination following a chemical spill, factors such as the following should be examined: the composition of the spilled chemical, its specific gravity, solubility, and viscosity; the soil's composition, pH , precipitation and infiltration rates; and hydrologic setting, the depth to the water table and its vertical and horizontal flow.
In addition to these factors, one must also take into account underlying uncertainties in data acquisition. For example, health databases and findings are typically derived from experimental or laboratory animal tests on a specific chemical or in connection with unrelated human studies. Therefore predictions from these studies may not be applicable to human subjects.
Sometimes lifestyle choices can also play a role in risk assessment . In addition to the chemical that a person is exposed to, other factors—such as smoking or exposure to another chemical substance—can have additive, accumulative, or antagonistic effects. Such anomalies require supplemental research, such as mathematical modeling .
Mathematical modeling mirrors the processes and interrelationships of real-life systems. The mathematical content of a model may extend to different equations or to simple look-up tables; its purpose is to reflect the required outcome of interest. For example, a health model frequently employed by the Environmental Protection Agency (EPA) is a Linearized Multistage Model because it yields the most conservative risk estimates when exposure occurs in very low doses. While ecological modeling is still in its infancy (because it involves outcomes at numerous levels from a single species to communities of organisms), models, nonetheless, provide valuable information to risk assessors.
[George M. Fell ]