Environmental regulatory models

It can be concluded that these models yield a satisfactory picture of the behavior and persistence of this PCB. The dominant processes are apparent. A new chemical of similar properties is unlikely to receive environmental regulatory approval, thus the model is apparently capable of identifying such chemicals prior to their dispersal into the environment. 

Advances in Validation of Environmental Exposure Model Predictions for Regulatory Purposes... 

The more difficult thing is to develop models that can, with reasonable confidence, be used to predict ecological effects. A detailed discussion of ecological approaches to risk assessment lies outside the scope of the present text. For further information, readers are referred to Suter (1993) Landis, Moore, and Norton (1998) and Peakall and Fairbrother (1998). One important question, already touched upon in this account, is to what extent biomarker assays can contribute to the risk assessment of environmental chemicals. The possible use of biomarkers for the assessment of chronic pollution and in regulatory toxicology is discussed by Handy, Galloway, and Depledge (2003). 

First, we investigate some of the regulatory motivations for chronic risk analysis. Next, it is necessary to point up the similarities and differences between acute and chronic risk and delineate the steps in estimating health risks posed by environmental chemicals. Following some illustrations of model structure, we conclude by discussing specific factors in fate analysis that suggest choices of model components. 

In the past few years a variety of workshops and symposia have been held on the subjects of model verification, field validation, field testing, etc. of mathematical models for the fate and transport of chemicals in various environmental media. Following a decade of extensive model development in this area, the emphasis has clearly shifted to answering the questions "How good are these models ", "How well do they represent natural systems ", and "Can they be used for management and regulatory decision-making "... 

The primary factor which governs the detection limit, the uncertainty of the blank, can differ markedly depending upon whether it is estimated from a model (intercept, baseline), or from a direct observation, or from "blind" interlaboratory comparison. Inappropriate blank evaluation, together with inadequate reporting of data when at or below the detection limit are seen as two of the most critical sources of information loss or information distortion, with serious potential effects in environmental and regulatory areas (3 ). 

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