The risk from an environmental contaminant is estimated with a risk equation involving the contaminant's concentration and other factors. Because the overall goal of risk management is to ensure such risks are not intolerably large, we need some way to backcalculate from constraints on risk mandated by regulation or prudence to the allowable environmental concentration for the contaminant. It is now well known that the approach used in deterministic assessments of simply inverting the risk equation to compute the cleanup goal does not work in a probabilistic assessmnent. Several approaches have been proposed, but none is sufficiently general. We have developed simple and efficient methods to compute cleanup goals that satisfy multiple simultaneous criteria in the context of a probabilistic assessment. The approach can be used with multiple receptors and with arbitrarily many constraints on percentiles of the target risk. The proposed research will explore the properties of this approach, conduct case studies to demonstrate its workability and extend it to more complex problems involving multiple effects (e.g., the toxicant causes two diseases), multiple trophic levels (e.g., plankton to shellfish to humans), and eventually to multiple exposure pathways (e.g., breathing, drinking, dermal absorption), and multiple potentially synergistic toxicants.
Commercialization would be in the from of a software library that could be used by software designers as well as end-users to compute the limits on concentrations of environmental contaminants that would yield tolerably low doses in humans under uncertainty and complex dynamics. The ability to compute such limits is needed by risk analysts in public health departments, potentially responsible"""""""" industries, and private consulting firms.
