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The objective of this study is to increase the understanding and transparency of chemical biotransfer modeling into meat and milk and explicitly confront the uncertainties in exposure assessments of chemicals that require such estimates. In cumulative exposure assessments that include food pathways, much of the overall uncertainty is attributable to the estimation of transfer into biota and through food webs. Currently, the most commonly used meat and milk-biotransfer models date back two decades and, in spite of their widespread use in multimedia exposure models, few attempts have been made to advance or improve the outdated and highly uncertain Kow regressions used in these models. Furthermore, in the range of Kow where meat and milk become the dominant human exposure pathways, these models often provide unrealistic rates and do not properly reflect the transfer dynamics. To address these issues, we developed a dynamic three-compartment cow model (called CKow), distinguishing lactating and nonlactating cows. For chemicals without available overall removal rates in the cow, a correlation is derived from measured values reported in the literature to predict this parameter from Kow. Results on carry over rates (COR) and biotransfer factors (BTF) demonstrate that a steady-state ratio between animal intake and meat concentrations is almost never reached. For meat, empirical data collected on short-term experiments need to be adjusted to provide estimates of average longer term behaviors. The performance of the new model in matching measurements is improved relative to existing models—thus reducing uncertainty. The CKow model is straightforward to apply at steady state for milk and dynamically for realistic exposure durations for meat COR. [ABSTRACT FROM AUTHOR]

A GIS-based procedure for predicting pesticide exposure in surface waters has been applied on a pilot river basin characterized by intensive agricultural activity. The predictive approach has been validated through experimental monitoring, performed by collecting manual and automatic water samples during the productive season. Five active ingredients (terbuthylazine, metolachlor, alachlor, linuron, fenitrothion) were selected for analysis to validate the predictive approach. Comparison between predicted and experimental values showed good agreement for terbuthylazine and metolachlor (used in large volumes within the basin), demonstrating the reliability of the approach. However, some anomalous results were obtained for some of the other chemicals, which serve to highlight the difficulties in getting reliable input data, in particular on application patterns (rate and time). Furthermore, the value of mapping pesticide exposure on the medium-large scale is described, and the limitations of the reported predictive approach are discussed. [ABSTRACT FROM AUTHOR]

The article discusses the use of the fumigant methyl iodide by farmers to kill pests in soils before planting crops such as strawberries. The state of California completed an assessment that indicated the use of methyl iodide poses a potential health risk to farm workers. The U.S. Environmental Protection Agency (EPA) approved the use of methyl iodide despite the recommendations by scientists to conduct an independent review concerning its potential danger.