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The article presents a few recent developments advanced by the authors in a few key areas of stochastic modeling of solute transport in heterogeneous aquifers. First, a brief review of the Lagrangian approach to modeling plumes longitudinal mass distribution and temporal (the breakthrough curve) mass arrival, is presented. Subsequently, transport in highly heterogeneous aquifers is analyzed by using a recently developed predictive model. It relates the non-Gaussian BTC to the permeability univariate pdf and integral scale, with application to the MADE field observations. Next, the approach is extended to transport of reactive solute, combinnig the effects of the random velocity field and multirate mass transfer on the BTC, with application to mass attenuation. The following topic is modeling of the local concentration field as affected by mixing and dilution due to pore scale dispersion. The results are applied to the analysis of concentration measurements at the Cape Cod field experiment. The last section incorporates the results of the preceding ones in health risk assessment by analyzing the impact of concentration prediction on risk uncertainty. It is illustrated by assessing the effect of identification of macrodispersivity from field characterization and transport modeling, upon the probability of health risk. [ABSTRACT FROM AUTHOR]

Do pollution emissions from livestock operations increase infant mortality rate (IMR)? A recent regression analysis of changes in IMR against changes in aggregate “animal units” (a weighted sum of cattle, pig, and poultry numbers) over time, for counties throughout the United States, suggested the provocative conclusion that they do: “[A] doubling of production leads to a 7.4% increase in infant mortality.” Yet, we find that regressing IMR changes against changes in specific components of “animal units” (cattle, pigs, and broilers) at the state level reveals statistically significant negative associations between changes in livestock production (especially, cattle production) and changes in IMR. We conclude that statistical associations between livestock variables and IMR variables are very sensitive to modeling choices (e.g., selection of explanatory variables, and use of specific animal types vs. aggregate “animal units). Such associations, whether positive or negative, do not warrant causal interpretation. We suggest that standard methods of quantitative risk assessment (QRA), including emissions release (source) models, fate and transport modeling, exposure assessment, and dose-response modeling, really are important—and indeed, perhaps, essential—for drawing valid causal inferences about health effects of exposures to guide sound, well-informed public health risk management policy. Reduced-form regression models, which skip most or all of these steps, can only quantify statistical associations (which may be due to model specification, variable selection, residual confounding, or other noncausal factors). Sound risk management requires the extra work needed to identify and model valid causal relations. [ABSTRACT FROM AUTHOR]